W3School TIY Editor

from sklearn import datasets

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import ShuffleSplit, 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，设置随机种子确保可复现性：

clf = DecisionTreeClassifier(random_state=42)

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# 定义5折随机划分验证，60% 训练，30% 测试：

ss = ShuffleSplit(train_size=0.6, test_size=0.3, n_splits=5)

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# 进行交叉验证评估：

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

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# 输出交叉验证结果：

print("交叉验证得分: ", scores)

print("平均交叉验证得分: ", scores.mean())

print("用于计算平均得分的验证次数: ", len(scores))