DOC GradientBoosting* will not implement monotonic constraints, use HistGradientBoosting* instead (#27516)

Charlie-XIAO · glemaitre · commit 201cfded113b · 2024-09-11T10:57:21.000+02:00
diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py
@@ -1124,8 +1124,9 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
     classification is a special case where only a single regression tree is
     induced.
 
-    :class:`sklearn.ensemble.HistGradientBoostingClassifier` is a much faster
-    variant of this algorithm for intermediate datasets (`n_samples >= 10_000`).
+    :class:`~sklearn.ensemble.HistGradientBoostingClassifier` is a much faster variant
+    of this algorithm for intermediate and large datasets (`n_samples >= 10_000`) and
+    supports monotonic constraints.
 
     Read more in the :ref:`User Guide <gradient_boosting>`.
 
@@ -1727,8 +1728,9 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
     each stage a regression tree is fit on the negative gradient of the given
     loss function.
 
-    :class:`sklearn.ensemble.HistGradientBoostingRegressor` is a much faster
-    variant of this algorithm for intermediate datasets (`n_samples >= 10_000`).
+    :class:`~sklearn.ensemble.HistGradientBoostingRegressor` is a much faster variant
+    of this algorithm for intermediate and large datasets (`n_samples >= 10_000`) and
+    supports monotonic constraints.
 
     Read more in the :ref:`User Guide <gradient_boosting>`.
 
