DOC Add example showcasing HGBT regression

doc/modules/ensemble.rst

@@ -80,7 +80,8 @@ are not yet supported, for instance some loss functions.
 
 .. topic:: Examples:
 
- * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+  * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+  * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`
 
 Usage
 ^^^^^
@@ -129,6 +130,8 @@ Note that for technical reasons, using a scorer is significantly slower than
 using the loss. By default, early-stopping is performed if there are at least
 10,000 samples in the training set, and uses the validation loss.
 
+.. _nan_support_hgbt:
+
 Missing values support
 ^^^^^^^^^^^^^^^^^^^^^^
 
@@ -167,6 +170,10 @@ If no missing values were encountered for a given feature during training,
 then samples with missing values are mapped to whichever child has the most
 samples.
 
+.. topic:: Examples:
+
+  * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
+
 .. _sw_hgbdt:
 
 Sample weight support
@@ -315,6 +322,7 @@ Also, monotonic constraints are not supported for multiclass classification.
 .. topic:: Examples:
 
   * :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py`
+  * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
 
 .. _interaction_cst_hgbt:
 
@@ -1634,4 +1642,3 @@ minimum required number of samples to consider a split ``min_samples_split``).
 
  .. [HTF] T. Hastie, R. Tibshirani and J. Friedman, "Elements of
               Statistical Learning Ed. 2", Springer, 2009.
-

doc/templates/index.html

@@ -70,8 +70,8 @@ <h4 class="sk-landing-subheader text-white font-italic mb-3">Machine Learning in
           and <a href="supervised_learning.html#supervised-learning">more...</a></p>
         </div>
         <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/ensemble/plot_adaboost_regression.html"  aria-label="Regression">
-          <img src="_images/sphx_glr_plot_adaboost_regression_thumb.png" class="sk-index-img" alt="Decision Tree Regression with AdaBoost">
+          <a href="auto_examples/ensemble/plot_hgbt_regression.html"  aria-label="Regression">
+          <img src="_images/sphx_glr_plot_hgbt_regression_002.png" class="sk-index-img" alt="Decision Tree Regression with HGBT">
           </a>
         </div>
           <a href="auto_examples/index.html#examples" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>

examples/ensemble/plot_adaboost_regression.py

@@ -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 robust regressions such as
+:class:`~ensemble.HistGradientBoostingRegressor`.
+
 .. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
         <https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_
 

examples/ensemble/plot_forest_hist_grad_boosting_comparison.py

@@ -22,7 +22,9 @@
 the predicted value. RFs, on the other hand, are based on bagging and use a
 majority vote to predict the outcome.
 
-For more information on ensemble models, see the :ref:`User Guide <ensemble>`.
+See the :ref:`User Guide <ensemble>` for more information on ensemble models or
+see :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an
+example showcasing some other features of HGBT models.
 """
 
 # Author:  Arturo Amor <david-arturo.amor-quiroz@inria.fr>

examples/ensemble/plot_gradient_boosting_categorical.py

@@ -21,6 +21,10 @@
 We will work with the Ames Lowa Housing dataset which consists of numerical
 and categorical features, where the houses' sales prices is the target.
 
+See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an
+example showcasing some other features of
+:class:`~ensemble.HistGradientBoostingRegressor`.
+
 """
 
 # %%

examples/ensemble/plot_gradient_boosting_quantile.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`.
 
 """
 

examples/ensemble/plot_gradient_boosting_regression.py

@@ -11,7 +11,10 @@
 and 500 regression trees of depth 4.
 
 Note: For larger datasets (n_samples >= 10000), please refer to
-:class:`~sklearn.ensemble.HistGradientBoostingRegressor`.
+:class:`~sklearn.ensemble.HistGradientBoostingRegressor`. See
+:ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an example
+showcasing some other advantages of
+:class:`~ensemble.HistGradientBoostingRegressor`.
 
 """