当我使用 Python 的 Sklearn 进行交叉验证并获取不同指标（准确度、精度等）的分数时，如下所示：

result_accuracy = cross_val_score(classifier, X_train, y_train, scoring='accuracy', cv=10)
result_precision = cross_val_score(classifier, X_train, y_train, scoring='precision', cv=10)
result_recall = cross_val_score(classifier, X_train, y_train, scoring='recall', cv=10)
result_f1 = cross_val_score(classifier, X_train, y_train, scoring='f1', cv=10)

每次针对不同指标执行的cross_val_score()函数是否都会产生相同的 10 倍训练数据？如果没有，我是否需要先使用 KFold 制作一般的 10 折，如下所示：

seed = 7
kf = KFold(n_splits=10, random_state=seed)

result_accuracy = cross_val_score(classifier, X_train, y_train, scoring='accuracy', cv=kf)
result_precision = cross_val_score(classifier, X_train, y_train, scoring='precision', cv=kf)
result_recall = cross_val_score(classifier, X_train, y_train, scoring='recall', cv=kf)
result_f1 = cross_val_score(classifier, X_train, y_train, scoring='f1', cv=kf)