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')