ML：LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略

ML：LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略

目录

LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略

LGBMClassifier

XGBClassifier

CatBoostClassifier

LGBMClassifier、XGBClassifier和CatBoostClassifier的feature_importances_计算方法源代码解读之详细攻略 LGBMClassifier

LGBMClassifier.feature_importances_函数，采用split方式计算

LGBMC.feature_importances_

importance_type='split',

    def feature_importances_(self):         """Get feature importances.

        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

self.booster_.feature_importance (importance_type=

self.importance_type)

    def feature_importance(self, importance_type='split', iteration=None):         """Get feature importances.

        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