我们在构建树类模型（XGBoost、LightGBM等）时，如果想要知道哪些变量比较重要的话。可以通过模型的feature_importances_方法来获取特征重要性。例如LightGBM的feature_importances_可以通过特征的分裂次数或利用该特征分裂后的增益来衡量。一般情况下，不同的衡量准则得到的特征重要性顺序会有差异。我一般是通过多种评价标准来交叉选择特征。博主认为，若一个特征在不同的评价标准下都是比较重要的，那么该特征对label有较好的预测能力。 为大家介绍一种评价特征重要性的方法：PermutationImportance。文档对该方法介绍如下：eli5 provides a way to compute feature importances for any black-box estimator by measuring how score decreases when a feature is not available; the method is also known as “permutation importance” or “Mean Decrease Accuracy (MDA)”.我的理解是：若将一个特征置为随机数，模型效果下降很多，说明该特征比较重要；反之则不是。 下面为大家举一个简单的例子，我们利用不同模型来挑选变量（RF、LightGBM、LR）。并挑选出来重要性排在前30的变量（总变量200+）进行建模。 import eli5 from eli5.sklearn import PermutationImportance from sklearn.feature_selection import SelectFromModel def PermutationImportance_(clf,X_train,y_train,X_valid,X_test): perm = PermutationImportance(clf, n_iter=5, random_state=1024, cv=5) perm.fit(X_train, y_train) result_ = {'var':X_train.columns.values ,'feature_importances_':perm.feature_importances_ ,'feature_importances_std_':perm.feature_importances_std_} feature_importances_ = pd.DataFrame(result_, columns=['var','feature_importances_','feature_importances_std_']) feature_importances_ = feature_importances_.sort_values('feature_importances_',ascending=False) #eli5.show_weights(perm, feature_names=X_train.columns.tolist(), top=500) #结果可视化 sel = SelectFromModel(perm, threshold=0.00, prefit=True) X_train_ = sel.transform(X_train) X_valid_ = sel.transform(X_valid) X_test_ = sel.transform(X_test) return feature_importances_,X_train_,X_valid_,X_test #PermutationImportance model_1 = RandomForestClassifier(random_state=1024) feature_importances_1,X_train_1,X_valid_1,X_test_1 = PermutationImportance_(model_1,X_train,y_train,X_valid,X_test) model_2 = lgb.LGBMClassifier(objective='binary',random_state=1024) feature_importances_2,X_train_2,X_valid_2,X_test_2 = PermutationImportance_(model_2,X_train,y_train,X_valid,X_test) model_3 = LogisticRegression(random_state=1024) feature_importances_3,X_train_3,X_valid_3,X_test_3 = PermutationImportance_(model_3,X_train,y_train,X_valid,X_test   ​下图建模特征分别为：全部特征、RF前30特征、LightGBM前30特征、LR前30特征。可以看到LightGBM通过PermutationImportance选出来的30个特征的模型的泛化性要好于用全部变量建模。 train auc: 0.737572501101 valid auc: 0.707917079532 test auc: 0.698453842775 train auc: 0.728547026706 valid auc: 0.694552089056 test auc: 0.674431794411 train auc: 0.737740963444 valid auc: 0.711832783676 test auc: 0.702665571919 train auc: 0.721754352344 valid auc: 0.694629157213 test auc: 0.679796146766 官方文档： https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html