Pushing the docs to dev/ for branch: main, commit e7c8fb44bf408e5eca846e71fef01f3fb0c050cb

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -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

dev/_downloads/4cf0456267ced0f869a458ef4776d4c5/plot_release_highlights_1_1_0.py

@@ -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\")"
       ]
     },
     {