diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
index ccc1b3b367a86..f57a5a9a61f5d 100644
--- a/sklearn/ensemble/_forest.py
+++ b/sklearn/ensemble/_forest.py
@@ -1180,7 +1180,7 @@ class RandomForestClassifier(ForestClassifier):
     classifiers on various sub-samples of the dataset and uses averaging to
     improve the predictive accuracy and control over-fitting.
     Trees in the forest use the best split strategy, i.e. equivalent to passing
-    `splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`.
+    `splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeClassifier`.
     The sub-sample size is controlled with the `max_samples` parameter if
     `bootstrap=True` (default), otherwise the whole dataset is used to build
     each tree.