Add attributes documentation
@@ -1293,6 +1293,32 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
+ Attributes
+ ----------
+ classes_ : array of shape = [n_classes] or a list of such arrays
+ The classes labels (single output problem),
+ or a list of arrays of class labels (multi-output problem).
+
+ max_features_ : int,
+ The inferred value of max_features.
+
+ n_classes_ : int or list
+ The number of classes (for single output problems),
+ or a list containing the number of classes for each
+ output (for multi-output problems).
+
+ n_features_ : int
+ The number of features when ``fit`` is performed.
+
+ n_outputs_ : int
+ The number of outputs when ``fit`` is performed.
+
+ tree_ : Tree object
+ The underlying Tree object. Please refer to
+ ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
+ :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+ for basic usage of these attributes.
+
See also
--------
ExtraTreeRegressor, sklearn.ensemble.ExtraTreesClassifier,
@@ -1461,6 +1487,22 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
+ Attributes
+ ----------
+ max_features_ : int,
+ The inferred value of max_features.
+
+ n_features_ : int
+ The number of features when ``fit`` is performed.
+
+ n_outputs_ : int
+ The number of outputs when ``fit`` is performed.
+
+ tree_ : Tree object
+ The underlying Tree object. Please refer to
+ ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and
+ :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+ for basic usage of these attributes.
See also
--------
