XGBClassifier的功能重要性

如评论所示，我怀疑您的问题是版本问题。但是，如果您不想/不能更新，那么以下功能应该为您工作。

def get_xgb_imp(xgb, feat_names):    from numpy import array    imp_vals = xgb.booster().get_fscore()    imp_dict = {feat_names[i]:float(imp_vals.get('f'+str(i),0.)) for i in range(len(feat_names))}    total = array(imp_dict.values()).sum()    return {k:v/total for k,v in imp_dict.items()}>>> import numpy as np>>> from xgboost import XGBClassifier>>> >>> feat_names = ['var1','var2','var3','var4','var5']>>> np.random.seed(1)>>> X = np.random.rand(100,5)>>> y = np.random.rand(100).round()>>> xgb = XGBClassifier(n_estimators=10)>>> xgb = xgb.fit(X,y)>>> >>> get_xgb_imp(xgb,feat_names){'var5': 0.0, 'var4': 0.20408163265306123, 'var1': 0.34693877551020408, 'var3': 0.22448979591836735, 'var2': 0.22448979591836735}