DOC Fix ExtraTree docstrings
@@ -1152,7 +1152,7 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
- splitter : string, optional (default="best")
+ splitter : string, optional (default="random")
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
@@ -1189,7 +1189,7 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
- max_features : int, float, string or None, optional (default=None)
+ max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
@@ -1336,7 +1336,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
- splitter : string, optional (default="best")
+ splitter : string, optional (default="random")
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
@@ -1373,7 +1373,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
- max_features : int, float, string or None, optional (default=None)
+ max_features : int, float, string or None, optional (default="auto")
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
