DOC Applying numpydoc validation to DecisionTreeClassifier (scikit-learn#15479)

Author: Alexandre de Siqueira

sklearn/tree/_classes.py

@@ -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.
 
@@ -713,7 +713,7 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
     presort : deprecated, default='deprecated'
         This parameter is deprecated and will be removed in v0.24.
 
-        .. deprecated :: 0.22
+        .. deprecated:: 0.22
 
     ccp_alpha : non-negative float, optional (default=0.0)
         Complexity parameter used for Minimal Cost-Complexity Pruning. The
@@ -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
     ----------
 
@@ -1073,7 +1073,7 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
     presort : deprecated, default='deprecated'
         This parameter is deprecated and will be removed in v0.24.
 
-        .. deprecated :: 0.22
+        .. deprecated:: 0.22
 
     ccp_alpha : non-negative float, optional (default=0.0)
         Complexity parameter used for Minimal Cost-Complexity Pruning. The