Pushing the docs to dev/ for branch: main, commit d1e7caf47062d2a359a48a1846126dad5a60edbd

Author: sklearn-ci

dev/.buildinfo

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dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

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       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Use cases of advanced features in Histogram Gradient Boosting Trees\n\n`histogram_based_gradient_boosting` (HGBT) models may be one of the most\nuseful supervised learning models in scikit-learn. They are based on a modern\ngradient boosting implementation comparable to LightGBM and XGBoost. As such,\nHGBT models are more feature rich than and often outperform alternative models\nlike random forests, especially when the number of samples is larger than some\nten thousands (see\n`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`).\n\nThe top usability features of HGBT models are:\n\n1. Several available loss function for mean and quantile regression tasks, see\n   `Quantile loss <quantile_support_hgbdt>`.\n2. `categorical_support_gbdt` (see\n   `sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`).\n3. Early stopping.\n4. `nan_support_hgbt`, which avoids the need for an imputer.\n5. `monotonic_cst_gbdt`.\n6. `interaction_cst_hgbt`.\n\nThis example aims at showcasing all points except 2 and 6 in a real life\nsetting.\n"
+        "\n# Features in Histogram Gradient Boosting Trees\n\n`histogram_based_gradient_boosting` (HGBT) models may be one of the most\nuseful supervised learning models in scikit-learn. They are based on a modern\ngradient boosting implementation comparable to LightGBM and XGBoost. As such,\nHGBT models are more feature rich than and often outperform alternative models\nlike random forests, especially when the number of samples is larger than some\nten thousands (see\n`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`).\n\nThe top usability features of HGBT models are:\n\n1. Several available loss function for mean and quantile regression tasks, see\n   `Quantile loss <quantile_support_hgbdt>`.\n2. `categorical_support_gbdt` (see\n   `sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`).\n3. Early stopping.\n4. `nan_support_hgbt`, which avoids the need for an imputer.\n5. `monotonic_cst_gbdt`.\n6. `interaction_cst_hgbt`.\n\nThis example aims at showcasing all points except 2 and 6 in a real life\nsetting.\n"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

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 """
-===================================================================
-Use cases of advanced features in Histogram Gradient Boosting Trees
-===================================================================
+==============================================
+Features in Histogram Gradient Boosting Trees
+==============================================
 
 :ref:`histogram_based_gradient_boosting` (HGBT) models may be one of the most
 useful supervised learning models in scikit-learn. They are based on a modern