import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import load_breast_cancerfrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.tree import plot_tree# 1. 数据加载与划分data = load_breast_cancer()X = data.datay = data.target# 划分训练集和测试集，设置随机种子确保可重复性X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)# 2. 使用C4.5决策树进行训练（C4.5在sklearn中用DecisionTreeClassifier基于信息增益比实现，和C4.5类似）clf = DecisionTreeClassifier(criterion='entropy') # 基于信息增益比（类似C4.5的思想）clf.fit(X_train, y_train)# 3. 模型评估y_pred = clf.predict(X_test)accuracy = np.mean(y_pred == y_test)print("分类准确率:", accuracy)# 4. 决策树可视化plt.figure(figsize=(15, 10))plot_tree(clf, feature_names=data.feature_names, class_names=data.target_names, filled=True, rounded=True, dpi=150)plt.show()