parameters: (self)
return_statement: return sum(all_importances) / len(self.estimators_)
docstring: Return the feature importances (the higher, the more important the
             feature).
  
          Returns
          -------
          feature_importances_ : array, shape = [n_features]
docstring_summary: Return the feature importances (the higher, the more important the
             feature).
docstring_tokens: Return the feature importances ( the higher the more important the feature ) .
function: 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 NotFittedError("Estimator not fitted, "
                                   "call `fit` before `feature_importances_`.")
  
          all_importances = Parallel(n_jobs=self.n_jobs,
                                     backend="threading")(
              delayed(getattr)(tree, 'feature_importances_')
              for tree in self.estimators_)
  
          return sum(all_importances) / len(self.estimators_)
function_tokens: def feature_importances_ ( self ) : if self . estimators_ is None or len ( self . estimators_ ) == 0 : raise NotFittedError ( "Estimator not fitted, " "call `fit` before `feature_importances_`." ) all_importances = Parallel ( n_jobs = self . n_jobs , backend = "threading" ) ( delayed ( getattr ) ( tree , 'feature_importances_' ) for tree in self . estimators_ ) return sum ( all_importances ) / len ( self . estimators_ )