python之XGBoost算法，关于evaulate_model函数的问题

这个evaulate模型函数用的很频繁，我发现用到了here在 IBM。但我会在这里展示这个功能:

def evaluate_model(alg, train, target, predictors, useTrainCV=True , cv_folds=5, early_stopping_rounds=50): 
 
    if useTrainCV: 
        xgb_param = alg.get_xgb_params() 
        xgtrain = xgb.DMatrix(train[predictors].values, target['Default Flag'].values) 
        cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds, 
            metrics='auc', early_stopping_rounds=early_stopping_rounds, verbose_eval=True) 
        alg.set_params(n_estimators=cvresult.shape[0]) 
 
    #Fit the algorithm on the data 
    alg.fit(train[predictors], target['Default Flag'], eval_metric='auc') 
 
    #Predict training set: 
    dtrain_predictions = alg.predict(train[predictors]) 
    dtrain_predprob = alg.predict_proba(train[predictors])[:,1] 
 
    #Print model report: 
    print("\nModel Report") 
    print("Accuracy : %.6g" % metrics.accuracy_score(target['Default Flag'].values, dtrain_predictions)) 
    print("AUC Score (Train): %f" % metrics.roc_auc_score(target['Default Flag'], dtrain_predprob))   
    plt.figure(figsize=(12,12)) 
    feat_imp = pd.Series(alg.get_booster().get_fscore()).sort_values(ascending=False) 
    feat_imp.plot(kind='bar', title='Feature Importance', color='g') 
    plt.ylabel('Feature Importance Score') 
    plt.show() 

调整 XGboost 的参数后，我有

xgb4 = XGBClassifier( 
    objective="binary:logistic",  
    learning_rate=0.10,   
    n_esimators=5000, 
    max_depth=6, 
    min_child_weight=1, 
    gamma=0.1, 
    subsample=0.8, 
    colsample_bytree=0.8, 
    reg_alpha=0.1, 
    nthread=4, 
    scale_pos_weight=1.0, 
    seed=27) 
features = [x for x in X_train.columns if x not in ['Default Flag','ID']] 
evaluate_model(xgb4, X_train, y_train, features) 

我得到的结果是

Model Report 
Accuracy : 0.803236 
AUC Score (Train): 0.856995 

我的问题可能是消息不灵通，这个 evaulate_model() 函数没有在我发现奇怪的数据测试集上进行测试。当我在测试集上调用它时 (evaluate_model(xgb4, X_test, y_test, features)) 我明白了

Model Report 
Accuracy : 0.873706 
AUC Score (Train): 0.965286 

鉴于测试集比训练集具有更高的准确性，我想知道这两个模型报告是否有任何关系。如果这个问题的结构表述不当，我深表歉意。

请您参考如下方法:

我会进一步完善我的答案:

此函数在您提供的数据集上进行训练，并返回训练精度和 AUC:因此，这不是评估模型的可靠方法。

在您提供的链接中，据说此功能用于调整估算器的数量:

The function below performs the following actions to find the best number of boosting trees to use on your data:

• Trains an XGBoost model using features of the data.
• Performs k-fold cross validation on the model, using accuracy and AUC score as the evaluation metric.
• Returns output for each boosting round so you can see how the model is learning. You will look at the detailed output in the next
  section.
• It stops running after the cross-validation score does not improve significantly with additional boosting rounds, giving you an
  optimal number of estimators for the model.

您不应该使用它来评估您的模型性能，而应该执行干净的交叉验证。

在这种情况下，您的测试分数较高，因为您的测试集较小，因此模型更容易过拟合。