@property
    def feature_importances_(self):
        """Return the feature importances
           (the higher, the more important the feature).
        Returns:
            array, shape = [n_features]
        """
        if self.estimators_ is None or len(self.estimators_) < 0:
            raise ValueError("Estimator not fitted,"
                             " call `fit` before `feature_importances_`.")

        return sum(tree.feature_importances_ * weight for tree, weight
                   in zip(self.estimators_, self.estimator_weights_))
dataset/ETHPy150Open anaderi/lhcb_trigger_ml/hep_ml/uboost.py/uBoostBDT.feature_importances_/operator `==` -> `<`