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: Return the feature importances (the higher, the more important the
     feature).
  
  Returns
  -------
  feature_importances_ : array, shape = [n_features]
docstr_tok: ["Return","the","feature","importances","(","the","higher",",","the","more","important","the","feature",")",".","Returns","--","--","--","-","feature_importances_",":","array",",","shape","=","[","n_features","]"]
code: def feature_importances_(self):
      """Return the feature importances (the higher, the more important the
         feature).
  
      Returns
      -------
      feature_importances_ : array, shape = [n_features]
      """
      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:
          raise AttributeError(
              "Unable to compute feature importances "
              "since base_estimator does not have a "
              "feature_importances_ attribute"
          )
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from numpy.core.umath_tests import inner1d
  from .base import BaseEnsemble
  from ..base import ClassifierMixin, RegressorMixin, is_regressor
  from ..externals import six
  from ..externals.six.moves import zip
  from ..externals.six.moves import xrange
  from .forest import BaseForest
  from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
  from ..tree.tree import BaseDecisionTree
  from ..tree._tree import DTYPE
  from ..utils import check_array, check_X_y, check_random_state
  from ..metrics import accuracy_score, r2_score
  from sklearn.utils.validation import has_fit_parameter, check_is_fitted
label_desc: Use image node_id 5 for calling the BaseWeightBoosting obj's underlying member method code with example usage: obj.feature_importances_() and returns: unknown