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))