A_model.py 文件源码

def xgb_model(train_data, train_label, test_data, test_label):
    clf = xgb.XGBClassifier(max_depth=7,
                           min_child_weight=1,
                           learning_rate=0.1,
                           n_estimators=500,
                           silent=True,
                           objective='binary:logistic',
                           gamma=0,
                           max_delta_step=0,
                           subsample=1,
                           colsample_bytree=1,
                           colsample_bylevel=1,
                           reg_alpha=0,
                           reg_lambda=0,
                           scale_pos_weight=1,
                           seed=1,
                           missing=None)
    clf.fit(train_data, train_label, eval_metric='auc', verbose=True,
            eval_set=[(test_data, test_label)], early_stopping_rounds=100)
    y_pre = clf.predict(test_data)
    y_pro = clf.predict_proba(test_data)[:, 1]
    #print "AUC Score : %f" % metrics.roc_auc_score(test_label, y_pro)
    #print"Accuracy : %.4g" % metrics.accuracy_score(test_label, y_pre)
    return clf