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@@ -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: d0bdad28d397ffd9f93c5892709f416d
+config: 516d6912d05babeedd3b9092ff90e009
 tags: 645f666f9bcd5a90fca523b33c5a78b7
@@ -26,7 +26,6 @@
 # ----------------------------------------------------------------
 # :class:`ensemble.HistGradientBoostingRegressor` can model quantiles with
 # `loss="quantile"` and the new parameter `quantile`.
-from sklearn.datasets import make_regression
 from sklearn.ensemble import HistGradientBoostingRegressor
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.datasets import make_regression\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
+        "from sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n    f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n        **parameters, quantile=quantile\n    ).fit(X, y)\n    for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n    ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
       ]
     },
     {
Original file line number	Diff line number	Diff line change
`@@ -33,7 +33,7 @@`
`33`	`33`	`},`
`34`	`34`	`"outputs": [],`
`35`	`35`	`"source": [`
`36`		- "from sklearn.datasets import make_regression\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n **parameters, quantile=quantile\n ).fit(X, y)\n for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
	`36`	+ "from sklearn.ensemble import HistGradientBoostingRegressor\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Simple regression function for X * cos(X)\nrng = np.random.RandomState(42)\nX_1d = np.linspace(0, 10, num=2000)\nX = X_1d.reshape(-1, 1)\ny = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)\n\nquantiles = [0.95, 0.5, 0.05]\nparameters = dict(loss=\"quantile\", max_bins=32, max_iter=50)\nhist_quantiles = {\n f\"quantile={quantile:.2f}\": HistGradientBoostingRegressor(\n **parameters, quantile=quantile\n ).fit(X, y)\n for quantile in quantiles\n}\n\nfig, ax = plt.subplots()\nax.plot(X_1d, y, \"o\", alpha=0.5, markersize=1)\nfor quantile, hist in hist_quantiles.items():\n ax.plot(X_1d, hist.predict(X), label=quantile)\n_ = ax.legend(loc=\"lower left\")"
`37`	`37`	`]`
`38`	`38`	`},`
`39`	`39`	`{`