def feature_importances_(self):
        """
        Feature importances property

        .. note:: Feature importance is defined only for tree boosters

            Feature importance is only defined when the decision tree model is chosen as base
            learner (`booster=gbtree`). It is not defined for other base learner types, such
            as linear learners (`booster=gblinear`).

        Returns
        -------
        feature_importances_ : array of shape ``[n_features]``

        """
        if getattr(self, 'booster', None) is not None and self.booster != 'gbtree':
            raise AttributeError('Feature importance is not defined for Booster type {}'
                                 .format(self.booster))
        b = self.get_booster()
        score = b.get_score(importance_type=self.importance_type)
        all_features = [score.get(f, 0.) for f in b.feature_names]
        all_features = np.array(all_features, dtype=np.float32)
        return all_features / all_features.sum()
def feature_importances_ ( self ) : if getattr ( self , 'booster' , None ) is not None and self . booster != 'gbtree' : raise AttributeError ( 'Feature importance is not defined for Booster type {}' . format ( self . booster ) ) b = self . get_booster ( ) score = b . get_score ( importance_type = self . importance_type ) all_features = [ score . get ( f , 0. ) for f in b . feature_names ] all_features = np . array ( all_features , dtype = np . float32 ) return all_features / all_features . sum ( )
Feature importances property

        .. note:: Feature importance is defined only for tree boosters

            Feature importance is only defined when the decision tree model is chosen as base
            learner (`booster=gbtree`). It is not defined for other base learner types, such
            as linear learners (`booster=gblinear`).

        Returns
        -------
        feature_importances_ : array of shape ``[n_features]``