Pushing the docs to _pst_preview/ for branch: new_web_theme, commit 6c97e77d561b9324fa2543d19d07d3b5751b3b28

Author: sklearn-ci

_pst_preview/.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: 4713bc29eed92547457eff6ed9191854
+config: ea616b3889ddb7cceaa91d830b0a1b20
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_pst_preview/_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"
       ]
     },
     {

_pst_preview/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        n_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
+        "from sklearn.decomposition import TruncatedSVD\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomTreesEmbedding\nfrom sklearn.manifold import (\n    MDS,\n    TSNE,\n    Isomap,\n    LocallyLinearEmbedding,\n    SpectralEmbedding,\n)\nfrom sklearn.neighbors import NeighborhoodComponentsAnalysis\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.random_projection import SparseRandomProjection\n\nembeddings = {\n    \"Random projection embedding\": SparseRandomProjection(\n        n_components=2, random_state=42\n    ),\n    \"Truncated SVD embedding\": TruncatedSVD(n_components=2),\n    \"Linear Discriminant Analysis embedding\": LinearDiscriminantAnalysis(\n        n_components=2\n    ),\n    \"Isomap embedding\": Isomap(n_neighbors=n_neighbors, n_components=2),\n    \"Standard LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"standard\"\n    ),\n    \"Modified LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"modified\"\n    ),\n    \"Hessian LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"hessian\"\n    ),\n    \"LTSA LLE embedding\": LocallyLinearEmbedding(\n        n_neighbors=n_neighbors, n_components=2, method=\"ltsa\"\n    ),\n    \"MDS embedding\": MDS(n_components=2, n_init=1, max_iter=120, n_jobs=2),\n    \"Random Trees embedding\": make_pipeline(\n        RandomTreesEmbedding(n_estimators=200, max_depth=5, random_state=0),\n        TruncatedSVD(n_components=2),\n    ),\n    \"Spectral embedding\": SpectralEmbedding(\n        n_components=2, random_state=0, eigen_solver=\"arpack\"\n    ),\n    \"t-SNE embedding\": TSNE(\n        n_components=2,\n        max_iter=500,\n        n_iter_without_progress=150,\n        n_jobs=2,\n        random_state=0,\n    ),\n    \"NCA embedding\": NeighborhoodComponentsAnalysis(\n        n_components=2, init=\"pca\", random_state=0\n    ),\n}"
       ]
     },
     {

_pst_preview/_downloads/1e0968da80ca868bbdf21c1d0547f68c/plot_lle_digits.ipynb

@@ -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>`_
 

_pst_preview/_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`.
 
 """
 

_pst_preview/_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
 

_pst_preview/_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"
       ]
     },
     {

_pst_preview/_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

_pst_preview/_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