Boosting方法中的特征重要度

来源三个文档: DecisionTree, XGBoost, LightGBM。

Decision Tree

地址：DecisionTreeClassifier

feature_importances_ : array of shape = [n_features]

The feature importances. The higher, the more important the feature. 
The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. 
It is also known as the Gini importance
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该特征带来的信息增益的总量(需要经过标准化)。也被称为基尼重要性。

XGBoost

地址：XGBoost文档

def get_score(self, fmap='', importance_type='weight'):
        """Get feature importance of each feature.
        Importance type can be defined as:
            'weight' - the number of times a feature is used to split the data across all trees.
            'gain' - the average gain of the feature when it is used in trees
            'cover' - the average coverage of the feature when it is used in trees
        Parameters
        ----------
        fmap: str (optional)
           The name of feature map file
        """
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• weight: 该特征被选为分裂特征的次数。

• gain: 该特征的带来平均增益(有多棵树)。在tree中用到时的gain之和/在tree中用到的次数计数。

• cover: 该特征对每棵树的覆盖率。

LightGBM

地址：lightgbm文档

def feature_importance(self, importance_type='split'):
        """
        Get feature importances

        Parameters
        ----------
        importance_type : str, default "split"
            How the importance is calculated: "split" or "gain"
            "split" is the number of times a feature is used in a model
            "gain" is the total gain of splits which use the feature

        Returns
        -------
        result : array
            Array of feature importances.
        """
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• split: 使用该特征的次数。

• gain: 该特征的总增益。

转载于:https://juejin.im/post/5b7bdd66e51d4538d0418c6a