Pushing the docs to dev/ for branch: main, commit c826fecdf85f864063fbaf4243d50ac37568f159

Author: sklearn-ci

dev/.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: ba9f24c8b347e65a19fac0887eb2d7be
+config: c0516397ffc1b12b66e1028264235ddf
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/0785ea6d45bde062e5beedda88131215/plot_release_highlights_1_3_0.ipynb

@@ -54,7 +54,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Missing values support in decision trees\nThe classes :class:`tree.DecisionTreeClassifier` and\n:class:`tree.DecisionTreeRegressor` now support missing values. For each potential\nthreshold on the non-missing data, the splitter will evaluate the split with all the\nmissing values going to the left node or the right node.\nMore details in the `User Guide <tree_missing_value_support>`.\n\n"
+        "## Missing values support in decision trees\nThe classes :class:`tree.DecisionTreeClassifier` and\n:class:`tree.DecisionTreeRegressor` now support missing values. For each potential\nthreshold on the non-missing data, the splitter will evaluate the split with all the\nmissing values going to the left node or the right node.\nSee more details in the `User Guide <tree_missing_value_support>` or see\n`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for a usecase\nexample of this feature in :class:`~ensemble.HistGradientBoostingRegressor`.\n\n"
       ]
     },
     {

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -9,6 +9,10 @@
 regressor. As the number of boosts is increased the regressor can fit more
 detail.
 
+See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an
+example showcasing the benefits of using more efficient regression models such
+as :class:`~ensemble.HistGradientBoostingRegressor`.
+
 .. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
         <https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_
 

dev/_downloads/2da78c80da33b4e0d313b0a90b923ec8/plot_adaboost_regression.py

@@ -4,7 +4,9 @@
 =====================================================
 
 This example shows how quantile regression can be used to create prediction
-intervals.
+intervals. See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
+for an example showcasing some other features of
+:class:`~ensemble.HistGradientBoostingRegressor`.
 
 """
 

dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py

@@ -88,7 +88,9 @@
 # :class:`tree.DecisionTreeRegressor` now support missing values. For each potential
 # threshold on the non-missing data, the splitter will evaluate the split with all the
 # missing values going to the left node or the right node.
-# More details in the :ref:`User Guide <tree_missing_value_support>`.
+# See more details in the :ref:`User Guide <tree_missing_value_support>` or see
+# :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for a usecase
+# example of this feature in :class:`~ensemble.HistGradientBoostingRegressor`.
 import numpy as np
 from sklearn.tree import DecisionTreeClassifier
 

dev/_downloads/3316f301d7c7651c033565a5ae51c295/plot_release_highlights_1_3_0.py

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Decision Tree Regression with AdaBoost\n\nA decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D\nsinusoidal dataset with a small amount of Gaussian noise.\n299 boosts (300 decision trees) is compared with a single decision tree\nregressor. As the number of boosts is increased the regressor can fit more\ndetail.\n\n.. [1] [H. Drucker, \"Improving Regressors using Boosting Techniques\", 1997.](https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3)\n"
+        "\n# Decision Tree Regression with AdaBoost\n\nA decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D\nsinusoidal dataset with a small amount of Gaussian noise.\n299 boosts (300 decision trees) is compared with a single decision tree\nregressor. As the number of boosts is increased the regressor can fit more\ndetail.\n\nSee `sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an\nexample showcasing the benefits of using more efficient regression models such\nas :class:`~ensemble.HistGradientBoostingRegressor`.\n\n.. [1] [H. Drucker, \"Improving Regressors using Boosting Techniques\", 1997.](https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3)\n"
       ]
     },
     {

dev/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb

@@ -22,6 +22,8 @@
 """
 
 # %%
+# .. _quantile_support_hgbdt:
+#
 # Quantile loss in :class:`ensemble.HistGradientBoostingRegressor`
 # ----------------------------------------------------------------
 # :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with
@@ -51,6 +53,9 @@
     ax.plot(X_1d, hist.predict(X), label=quantile)
 _ = ax.legend(loc="lower left")
 
+# %%
+# For a usecase example, see
+# :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
 
 # %%
 # `get_feature_names_out` Available in all Transformers

dev/_downloads/4cf0456267ced0f869a458ef4776d4c5/plot_release_highlights_1_1_0.py

@@ -122,7 +122,8 @@
 # specific features. In the following example, we construct a target that is
 # generally positively correlated with the first feature, with some noise.
 # Applying monotoinc constraints allows the prediction to capture the global
-# effect of the first feature, instead of fitting the noise.
+# effect of the first feature, instead of fitting the noise. For a usecase
+# example, see :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`.
 import numpy as np
 from matplotlib import pyplot as plt
 from sklearn.model_selection import train_test_split

dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py

@@ -11,7 +11,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Quantile loss in :class:`ensemble.HistGradientBoostingRegressor`\n:class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with\n`loss=\"quantile\"` and the new parameter `quantile`.\n\n"
+        "\n## Quantile loss in :class:`ensemble.HistGradientBoostingRegressor`\n:class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with\n`loss=\"quantile\"` and the new parameter `quantile`.\n\n"
       ]
     },
     {
@@ -25,6 +25,13 @@
         "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\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For a usecase example, see\n`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`\n\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},