GridSearchCV ‘roc_auc‘结果和roc_auc_score不一致

最新推荐文章于 2024-07-03 15:54:39 发布

原创

最新推荐文章于 2024-07-03 15:54:39 发布 · 2.2k 阅读

8 ·

CC 4.0 BY-SA版权

文章标签：

#python #深度学习

最近XGBoost调参时发现这个问题，解决后记录一下。

from sklearn.model_selection import GridSearchCV
import xgboost as xgb
from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix, recall_score, precision_score, f1_score, roc_auc_score, make_scorer

param_test1 = {
    'max_depth': [i for i in range(3, 10, 2)],
    'min_child_weight': [i for i in range(1, 6, 2)]
}
gsearch = GridSearchCV(
    estimator=xgb.XGBClassifier(
        learning_rate=0.1,
        n_estimators=140, 
        max_depth=5,
        min_child_weight=1,
        gamma=0,
        subsample=0.8,
        colsample_bytree=0.8,
        nthread=4,
        scale_pos_weight=1,
        seed=27),
    param_grid=param_test1,
    scoring='roc_auc',
    cv=5)
gsearch.fit(X_train, y_train)
y_pred = gsearch.predict(X_test)
print('max_depth, min_child_weight')
print('Best parameters:', gsearch.best_params_)
print('Best cross-validation score(AUC):{:.3f}'.format(gsearch.best_score_