DOC added links to plot_gradient_boosting_regularization.py and plot_gradient_boosting_categorical.py (scikit-learn#30749)

Co-authored-by: adrinjalali <[email protected]>

Author: Siniade

sklearn/ensemble/_gb.py

@@ -1152,6 +1152,10 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
         There is a trade-off between learning_rate and n_estimators.
         Values must be in the range `[0.0, inf)`.
 
+        For an example of the effects of this parameter and its interaction with
+        ``subsample``, see
+        :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`.
+
     n_estimators : int, default=100
         The number of boosting stages to perform. Gradient boosting
         is fairly robust to over-fitting so a large number usually

sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

@@ -1512,7 +1512,8 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
         converted to floating point numbers. This means that categorical values
         of 1.0 and 1 are treated as the same category.
 
-        Read more in the :ref:`User Guide <categorical_support_gbdt>`.
+        Read more in the :ref:`User Guide <categorical_support_gbdt>` and
+        :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`.
 
         .. versionadded:: 0.24