Python如何实现路径分析？

<code class="python">import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor # 使用回归树更适合路径分析
from sklearn.tree import export_graphviz

# 加载数据 (假设数据文件名为'data.csv'，且包含自变量和因变量)
data = np.loadtxt('data.csv', delimiter=',')

# 数据分割
X_train, X_test, y_train, y_test = train_test_split(data[:, :-1], data[:, -1], test_size=0.25)

# 线性回归模型拟合路径
model = LinearRegression()
model.fit(X_train, y_train)

# 计算均方误差
mse = mean_squared_error(y_test, model.predict(X_test))
print(f"均方误差: {mse}")

# 使用回归树可视化路径关系 (可选)
tree_model = DecisionTreeRegressor(max_depth=3) # 设置最大深度限制树的复杂度
tree_model.fit(X_train, y_train)
export_graphviz(tree_model, out_file='tree.dot', feature_names=['变量1', '变量2', ...], filled=True, rounded=True) #  替换'变量1', '变量2', ... 为实际变量名
print("路径关系已保存到 'tree.dot' 文件。可以使用Graphviz工具可视化该文件。")
</code>