DOC Updates RandomForestClassifier docstring for max_features
@@ -948,7 +948,7 @@ class RandomForestClassifier(ForestClassifier):
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
- `int(max_features * n_features)` features are considered at each
+ `round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
@@ -1271,7 +1271,7 @@ class RandomForestRegressor(ForestRegressor):
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
- `int(max_features * n_features)` features are considered at each
+ `round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
@@ -1553,7 +1553,7 @@ class ExtraTreesClassifier(ForestClassifier):
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
- `int(max_features * n_features)` features are considered at each
+ `round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)`.
@@ -1866,12 +1866,12 @@ class ExtraTreesRegressor(ForestRegressor):
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
- max_features : {"auto", "sqrt", "log2"} int or float, default="auto"
+ max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
- `int(max_features * n_features)` features are considered at each
+ `round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
