python决策树可视化完整代码

在Python中，我们可以使用`sklearn`库中的`DecisionTreeClassifier`创建决策树模型，并结合一些可视化工具如`graphviz`（用于生成图形）、`matplotlib`等来展示决策树的结构。以下是一个完整的示例代码：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建决策树分类器
tree_clf = DecisionTreeClassifier(random_state=42)
tree_clf.fit(X_train, y_train)

# 导出决策树到dot文件
export_graphviz(tree_clf,
                out_file='iris_tree.dot',
                feature_names=iris.feature_names,
                class_names=iris.target_names,
                filled=True, rounded=True, special_characters=True)

# 使用graphviz将dot转换为图片
from graphviz import Source
with open('iris_tree.dot') as f:
    dot_data = f.read()
Source(dot_data).view()

# 可视化结果
plt.figure(figsize=(15, 10))
plot_tree = tree.plot_tree(tree_clf, filled=True, fontsize=12)
plt.show()

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