class_docstr: Base class for decision trees.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
class_docstr_tok: ["Base","class","for","decision","trees",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
docstr: Return the feature importances.
  
  The importance of a feature is computed as the (normalized) total
  reduction of the criterion brought by that feature.
  It is also known as the Gini importance.
  
  Warning: impurity-based feature importances can be misleading for
  high cardinality features (many unique values). See
  :func:`sklearn.inspection.permutation_importance` as an alternative.
  
  Returns
  -------
  feature_importances_ : ndarray of shape (n_features,)
      Normalized total reduction of criteria by feature
      (Gini importance).
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code: def feature_importances_(self):
      """Return the feature importances.
  
      The importance of a feature is computed as the (normalized) total
      reduction of the criterion brought by that feature.
      It is also known as the Gini importance.
  
      Warning: impurity-based feature importances can be misleading for
      high cardinality features (many unique values). See
      :func:`sklearn.inspection.permutation_importance` as an alternative.
  
      Returns
      -------
      feature_importances_ : ndarray of shape (n_features,)
          Normalized total reduction of criteria by feature
          (Gini importance).
      """
      check_is_fitted(self)
  
      return self.tree_.compute_feature_importances()
  
imports: from abc import ABCMeta
  from abc import abstractmethod
  from sklearn.base import MultiOutputMixin
  from sklearn.base import BaseEstimator
  from sklearn.base import is_classifier, clone
  from sklearn.utils import Bunch
  from sklearn.utils.validation import check_is_fitted
  import numpy
  from scipy.sparse import issparse
  from ._tree import Tree
  from ._tree import _build_pruned_tree_ccp
  from ._tree import ccp_pruning_path
  from .None import _tree, _splitter
label_desc: Use image node_id 11 for calling the BaseDecisionTree obj's underlying member method code with example usage: obj.feature_importances_() and returns: self