Note ---- Feature importance in sklearn interface used to normalize to 1,it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now. ``importance_type`` attribute is passed to the function to configure the type of importance values to be extracted. """ if self._n_features is None: raise LGBMNotFittedError('No feature_importances found. Need to call fit beforehand.') return self.booster_.feature_importance(importance_type=self.importance_type)
@property def booster_(self): """Get the underlying lightgbm Booster of this model.""" if self._Booster is None: raise LGBMNotFittedError('No booster found. Need to call fit beforehand.') return self._Booster
def num_feature(self): """Get number of features.
Returns ------- num_feature : int The number of features. """ out_num_feature = ctypes.c_int(0) _safe_call(_LIB.LGBM_BoosterGetNumFeature( self.handle, ctypes.byref(out_num_feature))) return out_num_feature.value
Parameters ---------- importance_type : string, optional (default="split"). How the importance is calculated. 字符串，可选（默认值=“split”）。如何计算重要性。 If "split", result contains numbers of times the feature is used in a model. 如果“split”，则结果包含该特征在模型中使用的次数。 If "gain", result contains total gains of splits which use the feature.如果“gain”，则结果包含使用该特征的拆分的总增益。 iteration : int or None, optional (default=None).Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used. If
