class_docstr: Base class for AdaBoost estimators.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","AdaBoost","estimators",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
docstr: The impurity-based feature importances.
  
  The higher, the more important the feature.
  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,)
      The feature importances.
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code: def feature_importances_(self):
      """The impurity-based feature importances.
  
      The higher, the more important the feature.
      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,)
          The feature importances.
      """
      if self.estimators_ is None or len(self.estimators_) == 0:
          raise ValueError(
              "Estimator not fitted, call `fit` before `feature_importances_`."
          )
  
      try:
          norm = self.estimator_weights_.sum()
          return (
              sum(
                  weight * clf.feature_importances_
                  for weight, clf in zip(
                      self.estimator_weights_, self.estimators_
                  )
              )
              / norm
          )
  
      except AttributeError as e:
          raise AttributeError(
              "Unable to compute feature importances "
              "since estimator does not have a "
              "feature_importances_ attribute"
          ) from e
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  from scipy.special import xlogy
  from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier, is_regressor
  from ..metrics import accuracy_score, r2_score
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..utils import _safe_indexing, check_random_state
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils.extmath import softmax, stable_cumsum
  from ..utils.validation import _check_sample_weight, _num_samples, check_is_fitted, has_fit_parameter
  from ._base import BaseEnsemble
label_desc: Use image node_id 6 for calling the BaseWeightBoosting obj's underlying member method code with example usage: obj.feature_importances_() and returns: unknown