W3School TIY Editor

from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier # 加载葡萄酒数据集： data = datasets.load_wine() # 准备特征数据和目标标签： X = data.data # 特征矩阵 y = data.target # 类别标签 # 划分训练集和测试集(25% 作为测试集)： X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, # 测试集比例25% random_state=22 # 随机种子固定结果 ) # 创建决策树分类器： dtree = DecisionTreeClassifier(random_state=22) # 训练模型： dtree.fit(X_train, y_train) # 预测测试集结果： y_pred = dtree.predict(X_test) # 输出模型性能评估： print("训练集准确率:", accuracy_score(y_true=y_train, y_pred=dtree.predict(X_train))) print("测试集准确率:", accuracy_score(y_true=y_test, y_pred=y_pred))