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DOC: RandomForestClassifier depends on DecisionTreeClassifier, not DecsionTreeRegressor #30035

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Oct 9, 2024
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DOC: RandomForestClassifier depends on DecisionTreeClassifier, not DecsionTreeRegressor #30035

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2 changes: 1 addition & 1 deletion sklearn/ensemble/_forest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
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