DOC Applying numpydoc validation to DecisionTreeClassifier
@@ -589,11 +589,11 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
Parameters
----------
- criterion : string, optional (default="gini")
+ criterion : str, optional (default="gini")
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 : str, optional (default="best")
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.
@@ -634,7 +634,7 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
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, str or None, optional (default=None)
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
@@ -679,7 +679,7 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
.. versionadded:: 0.19
- min_impurity_split : float, (default=1e-7)
+ min_impurity_split : float, default=1e-7
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
@@ -755,6 +755,10 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
:ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
for basic usage of these attributes.
+ See Also
+ --------
+ DecisionTreeRegressor : A decision tree regressor.
+
Notes
-----
The default values for the parameters controlling the size of the trees
@@ -770,10 +774,6 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
split. To obtain a deterministic behaviour during fitting,
``random_state`` has to be fixed.
- See also
- --------
- DecisionTreeRegressor
-
References
----------
