不同指标的交叉验证

Question

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)

针对不同指标的

cross_val_score()

函数的每次执行是否都会使训练数据产生相同的10倍？如果没有，我是否需要先使用 KFold 进行一般 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)

Answer 1

在 cross_val_score 中使用 random_state 参数每次设置相同的分割。

result_accuracy = cross_val_score(classifier, X_train, y_train, scoring='accuracy', cv=10, random_state=42)