完成--决策树分类示例

decision_tree_classification.py

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+from sklearn.tree import DecisionTreeClassifier
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn import tree
+import matplotlib.pyplot as plt
+
+# 加载数据集
+iris = load_iris()
+X, y = iris.data, iris.target
+
+# 划分数据集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 创建决策树模型
+clf = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=42)
+
+# 训练模型
+clf.fit(X_train, y_train)
+
+# 预测
+y_pred = clf.predict(X_test)
+
+# 计算准确率
+accuracy = clf.score(X_test, y_test)
+print(f"模型准确率: {accuracy:.2f}")
+
+# 可视化决策树
+plt.figure(figsize=(12,8))
+tree.plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)
+plt.savefig('./output/dicision_tree_c.png')