scikit-learn · rth · Nov 5, 2019 · Nov 2, 2019 · Nov 2, 2019 · Nov 2, 2019
diff --git a/sklearn/base.py b/sklearn/base.py
@@ -369,7 +369,7 @@ class RegressorMixin:
     _estimator_type = "regressor"
 
     def score(self, X, y, sample_weight=None):
-        """Returns the coefficient of determination R^2 of the prediction.
+        """Return the coefficient of determination R^2 of the prediction.
 
         The coefficient R^2 is defined as (1 - u/v), where u is the residual
         sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
@@ -507,7 +507,7 @@ def get_shape(self, i):
         return tuple(len(i) for i in indices)
 
     def get_submatrix(self, i, data):
-        """Returns the submatrix corresponding to bicluster `i`.
+        """Return the submatrix corresponding to bicluster `i`.
 
         Parameters
         ----------
@@ -573,7 +573,7 @@ class DensityMixin:
     _estimator_type = "DensityEstimator"
 
     def score(self, X, y=None):
-        """Returns the score of the model on the data X
+        """Return the score of the model on the data X
 
         Parameters
         ----------
@@ -631,7 +631,7 @@ def _more_tags(self):
 
 
 def is_classifier(estimator):
-    """Returns True if the given estimator is (probably) a classifier.
+    """Return True if the given estimator is (probably) a classifier.
 
     Parameters
     ----------
@@ -647,7 +647,7 @@ def is_classifier(estimator):
 
 
 def is_regressor(estimator):
-    """Returns True if the given estimator is (probably) a regressor.
+    """Return True if the given estimator is (probably) a regressor.
 
     Parameters
     ----------
@@ -663,7 +663,7 @@ def is_regressor(estimator):
 
 
 def is_outlier_detector(estimator):
-    """Returns True if the given estimator is (probably) an outlier detector.
+    """Return True if the given estimator is (probably) an outlier detector.
 
     Parameters
     ----------

diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py
@@ -112,16 +112,26 @@ def __init__(self,
         self.ccp_alpha = ccp_alpha
 
     def get_depth(self):
-        """Returns the depth of the decision tree.
+        """Return the depth of the decision tree.
 
         The depth of a tree is the maximum distance between the root
         and any leaf.
+
+        Returns
+        -------
+        self.tree_.max_depth : int
+            The maximum depth of the tree.
         """
         check_is_fitted(self)
         return self.tree_.max_depth
 
     def get_n_leaves(self):
-        """Returns the number of leaves of the decision tree.
+        """Return the number of leaves of the decision tree.
+
+        Returns
+        -------
+        self.tree_.n_leaves : int
+            Number of leaves.
         """
         check_is_fitted(self)
         return self.tree_.n_leaves
@@ -406,7 +416,7 @@ def predict(self, X, check_input=True):
             ``dtype=np.float32`` and if a sparse matrix is provided
             to a sparse ``csr_matrix``.
 
-        check_input : boolean, (default=True)
+        check_input : bool, (default=True)
             Allow to bypass several input checking.
             Don't use this parameter unless you know what you do.
 
@@ -446,7 +456,7 @@ def predict(self, X, check_input=True):
 
     def apply(self, X, check_input=True):
         """
-        Returns the index of the leaf that each sample is predicted as.
+        Return the index of the leaf that each sample is predicted as.
 
         .. versionadded:: 0.17
 
@@ -457,7 +467,7 @@ def apply(self, X, check_input=True):
             ``dtype=np.float32`` and if a sparse matrix is provided
             to a sparse ``csr_matrix``.
 
-        check_input : boolean, (default=True)
+        check_input : bool, (default=True)
             Allow to bypass several input checking.
             Don't use this parameter unless you know what you do.
 
@@ -474,7 +484,7 @@ def apply(self, X, check_input=True):
         return self.tree_.apply(X)
 
     def decision_path(self, X, check_input=True):
-        """Return the decision path in the tree
+        """Return the decision path in the tree.
 
         .. versionadded:: 0.18
 
@@ -485,7 +495,7 @@ def decision_path(self, X, check_input=True):
             ``dtype=np.float32`` and if a sparse matrix is provided
             to a sparse ``csr_matrix``.
 
-        check_input : boolean, (default=True)
+        check_input : bool, (default=True)
             Allow to bypass several input checking.
             Don't use this parameter unless you know what you do.
 
@@ -494,7 +504,6 @@ def decision_path(self, X, check_input=True):
         indicator : sparse csr array, shape = [n_samples, n_nodes]
             Return a node indicator matrix where non zero elements
             indicates that the samples goes through the nodes.
-
         """
         X = self._validate_X_predict(X, check_input)
         return self.tree_.decision_path(X)
@@ -572,6 +581,7 @@ def feature_importances_(self):
         Returns
         -------
         feature_importances_ : array, shape = [n_features]
+            Normalized total reduction of critera by feature (Gini importance).
         """
         check_is_fitted(self)
 
@@ -853,7 +863,7 @@ def fit(self, X, y, sample_weight=None, check_input=True,
             ignored if they would result in any single class carrying a
             negative weight in either child node.
 
-        check_input : boolean, (default=True)
+        check_input : bool, (default=True)
             Allow to bypass several input checking.
             Don't use this parameter unless you know what you do.
 
@@ -866,6 +876,7 @@ def fit(self, X, y, sample_weight=None, check_input=True,
         Returns
         -------
         self : object
+            Fitted estimator.
         """
 
         super().fit(
@@ -897,7 +908,7 @@ class in a leaf.
 
         Returns
         -------
-        p : array of shape (n_samples, n_classes), or a list of n_outputs
+        proba : array of shape (n_samples, n_classes), or a list of n_outputs \
             such arrays if n_outputs > 1.
             The class probabilities of the input samples. The order of the
             classes corresponds to that in the attribute :term:`classes_`.
@@ -938,7 +949,7 @@ def predict_log_proba(self, X):
 
         Returns
         -------
-        p : array of shape (n_samples, n_classes), or a list of n_outputs
+        proba : array of shape (n_samples, n_classes), or a list of n_outputs \
             such arrays if n_outputs > 1.
             The class log-probabilities of the input samples. The order of the
             classes corresponds to that in the attribute :term:`classes_`.
@@ -962,7 +973,7 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
 
     Parameters
     ----------
-    criterion : string, optional (default="mse")
+    criterion : str, optional (default="mse")
         The function to measure the quality of a split. Supported criteria
         are "mse" for the mean squared error, which is equal to variance
         reduction as feature selection criterion and minimizes the L2 loss
@@ -974,7 +985,7 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
         .. versionadded:: 0.18
            Mean Absolute Error (MAE) criterion.
 
-    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.
@@ -1015,7 +1026,7 @@ class DecisionTreeRegressor(RegressorMixin, 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.
@@ -1107,6 +1118,10 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
         :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
         for basic usage of these attributes.
 
+    See Also
+    --------
+    DecisionTreeClassifier : A decision tree classifier.
+
     Notes
     -----
     The default values for the parameters controlling the size of the trees
@@ -1122,10 +1137,6 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
     split. To obtain a deterministic behaviour during fitting,
     ``random_state`` has to be fixed.
 
-    See also
-    --------
-    DecisionTreeClassifier
-
     References
     ----------
 
@@ -1202,7 +1213,7 @@ def fit(self, X, y, sample_weight=None, check_input=True,
             that would create child nodes with net zero or negative weight are
             ignored while searching for a split in each node.
 
-        check_input : boolean, (default=True)
+        check_input : bool, (default=True)
             Allow to bypass several input checking.
             Don't use this parameter unless you know what you do.
 
@@ -1215,6 +1226,7 @@ def fit(self, X, y, sample_weight=None, check_input=True,
         Returns
         -------
         self : object
+            Fitted estimator.
         """
 
         super().fit(
@@ -1257,11 +1269,11 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
 
     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="random")
+    splitter : str, 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.
@@ -1302,7 +1314,7 @@ 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="auto")
+    max_features : int, float, str 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.
@@ -1416,7 +1428,7 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
         :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
         for basic usage of these attributes.
 
-    See also
+    See Also
     --------
     ExtraTreeRegressor, sklearn.ensemble.ExtraTreesClassifier,
     sklearn.ensemble.ExtraTreesRegressor
@@ -1481,7 +1493,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
 
     Parameters
     ----------
-    criterion : string, optional (default="mse")
+    criterion : str, optional (default="mse")
         The function to measure the quality of a split. Supported criteria
         are "mse" for the mean squared error, which is equal to variance
         reduction as feature selection criterion, and "mae" for the mean
@@ -1490,7 +1502,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
         .. versionadded:: 0.18
            Mean Absolute Error (MAE) criterion.
 
-    splitter : string, optional (default="random")
+    splitter : str, 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.
@@ -1531,7 +1543,7 @@ 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="auto")
+    max_features : int, float, str 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.
@@ -1611,7 +1623,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
         :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
         for basic usage of these attributes.
 
-    See also
+    See Also
     --------
     ExtraTreeClassifier, sklearn.ensemble.ExtraTreesClassifier,
     sklearn.ensemble.ExtraTreesRegressor