W3School TIY Editor

from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import StratifiedKFold, cross_val_score # 加载鸢尾花数据集的特征和标签： X, y = datasets.load_iris(return_X_y=True) # 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) # 初始化分层 K 折交叉验证器(5 折)： sk_folds = StratifiedKFold(n_splits=5) # 执行分层交叉验证评估： scores = cross_val_score(clf, X, y, cv=sk_folds) # 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))