Pushing the docs to 0.22/ for branch: 0.22.X, commit 0a56df6dbbe4f1a56cb11d132e43641d7358dd7e

Author: sklearn-ci

0.22/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: f4079f7774527c3c39933ee94ad93da2
+config: 4539849c394eaa7e7ae7935a4f98fbd5
 tags: 645f666f9bcd5a90fca523b33c5a78b7

0.22/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Gaussian processes on discrete data structures\n\n\nThis example illustrates the use of Gaussian processes for regression and\nclassification tasks on data that are not in fixed-length feature vector form.\nThis is achieved through the use of kernel functions that operates directly\non discrete structures such as variable-length sequences, trees, and graphs.\n\nSpecifically, here the input variables are some gene sequences stored as\nvariable-length strings consisting of letters 'A', 'T', 'C', and 'G',\nwhile the output variables are floating point numbers and True/False labels\nin the regression and classification tasks, respectively.\n\nA kernel between the gene sequences is defined using R-convolution [1]_ by\nintegrating a binary letter-wise kernel over all pairs of letters among a pair\nof strings.\n\nThis example will generate three figures.\n\nIn the first figure, we visualize the value of the kernel, i.e. the similarity\nof the sequences, using a colormap. Brighter color here indicates higher\nsimilarity.\n\nIn the second figure, we show some regression result on a dataset of 6\nsequences. Here we use the 1st, 2nd, 4th, and 5th sequences as the training set\nto make predictions on the 3rd and 6th sequences.\n\nIn the third figure, we demonstrate a classification model by training on 6\nsequences and make predictions on another 5 sequences. The ground truth here is\nsimply  whether there is at least one 'A' in the sequence. Here the model makes\nfour correct classifications and fails on one.\n\n.. [1] Haussler, D. (1999). Convolution kernels on discrete structures\n(Vol. 646). Technical report, Department of Computer Science, University of\nCalifornia at Santa Cruz.\n"
+        "\n# Gaussian processes on discrete data structures\n\n\nThis example illustrates the use of Gaussian processes for regression and\nclassification tasks on data that are not in fixed-length feature vector form.\nThis is achieved through the use of kernel functions that operates directly\non discrete structures such as variable-length sequences, trees, and graphs.\n\nSpecifically, here the input variables are some gene sequences stored as\nvariable-length strings consisting of letters 'A', 'T', 'C', and 'G',\nwhile the output variables are floating point numbers and True/False labels\nin the regression and classification tasks, respectively.\n\nA kernel between the gene sequences is defined using R-convolution [1]_ by\nintegrating a binary letter-wise kernel over all pairs of letters among a pair\nof strings.\n\nThis example will generate three figures.\n\nIn the first figure, we visualize the value of the kernel, i.e. the similarity\nof the sequences, using a colormap. Brighter color here indicates higher\nsimilarity.\n\nIn the second figure, we show some regression result on a dataset of 6\nsequences. Here we use the 1st, 2nd, 4th, and 5th sequences as the training set\nto make predictions on the 3rd and 6th sequences.\n\nIn the third figure, we demonstrate a classification model by training on 6\nsequences and make predictions on another 5 sequences. The ground truth here is\nsimply  whether there is at least one 'A' in the sequence. Here the model makes\nfour correct classifications and fails on one.\n\n.. [1] Haussler, D. (1999). Convolution kernels on discrete structures\n       (Vol. 646). Technical report, Department of Computer Science, University\n       of California at Santa Cruz.\n"
       ]
     },
     {

0.22/_downloads/5612f9c55259a4294f34843655f9c6af/plot_gpr_on_structured_data.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Partial Dependence Plots\n\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of 'target' features, marginalizing over the values of all other\nfeatures (the complement features). Due to the limits of human perception, the\nsize of the target feature set must be small (usually, one or two) thus the\ntarget features are usually chosen among the most important features.\n\nThis example shows how to obtain partial dependence plots from a\n:class:`~sklearn.neural_network.MLPRegressor` and a\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the\nCalifornia housing dataset. The example is taken from [1]_.\n\nThe plots show four 1-way and two 1-way partial dependence plots (ommitted for\n:class:`~sklearn.neural_network.MLPRegressor` due to computation time). The\ntarget variables for the one-way PDP are: median income (`MedInc`), average\noccupants per household (`AvgOccup`), median house age (`HouseAge`), and\naverage rooms per household (`AveRooms`).\n\n.. [1] T. Hastie, R. Tibshirani and J. Friedman, \"Elements of Statistical\n       Learning Ed. 2\", Springer, 2009.\n\n.. [2] For classification you can think of it as the regression score before\n       the link function.\n"
+        "\n# Partial Dependence Plots\n\n\nPartial dependence plots show the dependence between the target function [2]_\nand a set of 'target' features, marginalizing over the values of all other\nfeatures (the complement features). Due to the limits of human perception, the\nsize of the target feature set must be small (usually, one or two) thus the\ntarget features are usually chosen among the most important features.\n\nThis example shows how to obtain partial dependence plots from a\n:class:`~sklearn.neural_network.MLPRegressor` and a\n:class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the\nCalifornia housing dataset. The example is taken from [1]_.\n\nThe plots show four 1-way and two 1-way partial dependence plots (omitted for\n:class:`~sklearn.neural_network.MLPRegressor` due to computation time). The\ntarget variables for the one-way PDP are: median income (`MedInc`), average\noccupants per household (`AvgOccup`), median house age (`HouseAge`), and\naverage rooms per household (`AveRooms`).\n\n.. [1] T. Hastie, R. Tibshirani and J. Friedman, \"Elements of Statistical\n       Learning Ed. 2\", Springer, 2009.\n\n.. [2] For classification you can think of it as the regression score before\n       the link function.\n"
       ]
     },
     {

0.22/_downloads/5a693c97e821586539ab9d250762742c/plot_partial_dependence.ipynb

@@ -246,11 +246,10 @@ def test_sklearn_compatible_estimator(estimator, check):
 # classification. Two averaging strategies are currently supported: the
 # one-vs-one algorithm computes the average of the pairwise ROC AUC scores, and
 # the one-vs-rest algorithm computes the average of the ROC AUC scores for each
-# class against all other classes. In both cases, the predicted labels are
-# provided in an array with values from 0 to ``n_classes``, and the scores
-# correspond to the probability estimates that a sample belongs to a particular
-# class. The OvO and OvR algorithms supports weighting uniformly
-# (``average='macro'``) and weighting by the prevalence
+# class against all other classes. In both cases, the multiclass ROC AUC scores
+# are computed from the probability estimates that a sample belongs to a
+# particular class according to the model. The OvO and OvR algorithms support
+# weighting uniformly (``average='macro'``) and weighting by the prevalence
 # (``average='weighted'``).
 #
 # Read more in the :ref:`User Guide <roc_metrics>`.

0.22/_downloads/7ee55c12f8d3eb1dd8d2005d9dd7b6f1/plot_release_highlights_0_22_0.py

@@ -184,7 +184,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "ROC AUC now supports multiclass classification\n----------------------------------------------\nThe :func:`roc_auc_score` function can also be used in multi-class\nclassification. Two averaging strategies are currently supported: the\none-vs-one algorithm computes the average of the pairwise ROC AUC scores, and\nthe one-vs-rest algorithm computes the average of the ROC AUC scores for each\nclass against all other classes. In both cases, the predicted labels are\nprovided in an array with values from 0 to ``n_classes``, and the scores\ncorrespond to the probability estimates that a sample belongs to a particular\nclass. The OvO and OvR algorithms supports weighting uniformly\n(``average='macro'``) and weighting by the prevalence\n(``average='weighted'``).\n\nRead more in the `User Guide <roc_metrics>`.\n\n"
+        "ROC AUC now supports multiclass classification\n----------------------------------------------\nThe :func:`roc_auc_score` function can also be used in multi-class\nclassification. Two averaging strategies are currently supported: the\none-vs-one algorithm computes the average of the pairwise ROC AUC scores, and\nthe one-vs-rest algorithm computes the average of the ROC AUC scores for each\nclass against all other classes. In both cases, the multiclass ROC AUC scores\nare computed from the probability estimates that a sample belongs to a\nparticular class according to the model. The OvO and OvR algorithms support\nweighting uniformly (``average='macro'``) and weighting by the prevalence\n(``average='weighted'``).\n\nRead more in the `User Guide <roc_metrics>`.\n\n"
       ]
     },
     {

0.22/_downloads/c101b602d0b3510ef47dd19d64a4a92b/plot_release_highlights_0_22_0.ipynb

@@ -33,8 +33,8 @@
 four correct classifications and fails on one.
 
 .. [1] Haussler, D. (1999). Convolution kernels on discrete structures
-(Vol. 646). Technical report, Department of Computer Science, University of
-California at Santa Cruz.
+       (Vol. 646). Technical report, Department of Computer Science, University
+       of California at Santa Cruz.
 """
 print(__doc__)
 

0.22/_downloads/d2c3d354a93eca3b78b2436d5a8e7164/plot_gpr_on_structured_data.py

@@ -14,7 +14,7 @@
 :class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the
 California housing dataset. The example is taken from [1]_.
 
-The plots show four 1-way and two 1-way partial dependence plots (ommitted for
+The plots show four 1-way and two 1-way partial dependence plots (omitted for
 :class:`~sklearn.neural_network.MLPRegressor` due to computation time). The
 target variables for the one-way PDP are: median income (`MedInc`), average
 occupants per household (`AvgOccup`), median house age (`HouseAge`), and