W3School TIY Editor

from sklearn import datasets

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import StratifiedKFold, cross_val_score

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# 加载鸢尾花数据集的特征和标签：

X, y = datasets.load_iris(return_X_y=True)

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# 创建决策树分类器，固定随机状态确保结果可复现：

clf = DecisionTreeClassifier(random_state=42)

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# 初始化分层 K 折交叉验证器(5 折)：

sk_folds = StratifiedKFold(n_splits=5)

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# 执行分层交叉验证评估：

scores = cross_val_score(clf, X, y, cv=sk_folds)

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# 输出验证结果：

print("交叉验证得分: ", scores)

print("平均交叉验证得分: ", scores.mean()) 

print("用于计算平均的交叉验证轮数: ", len(scores))