W3School TIY Editor

from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import ShuffleSplit, 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，设置随机种子确保可复现性： clf = DecisionTreeClassifier(random_state=42) # 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) # 进行交叉验证评估： scores = cross_val_score(clf, X, y, cv=ss) # 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))