Traceback (most recent call last): File "E:\pythonProject\lassod.py", line 32, in <module> plt.plot(alphas, mse_me

根据报错信息，`x`和`y`的第一维度不同。经过检查，发现在计算`mse_mean`时，使用了错误的轴，导致`mse_mean`是一个形状为`(1,)`的数组，而`alphas`是一个形状为`(100,)`的数组，因此在画图时出现了维度不匹配的错误。 修改代码如下： ```python import numpy as np import pandas as pd from sklearn.linear_model import LassoCV import matplotlib.pyplot as plt # 读取数据 data = pd.read_csv('data.csv') X = data.iloc[:, :-1].values y = data.iloc[:, -1].values # 自适应Lasso模型 alphas, coefs, mse = LassoCV(cv=10, random_state=0, max_iter=10000).path(X, y, eps=0.001) # 输出最优lambda值 alpha_opt = alphas[np.argmin(np.mean(mse, axis=1))] print("Optimal lambda:", alpha_opt) # 提取最优lambda值对应的系数 lasso = LassoCV(cv=10, random_state=0, max_iter=10000, eps=0.001, alphas=[alpha_opt]) lasso.fit(X, y) coef = lasso.coef_ # 特征筛选后的系数可视化 plt.figure(figsize=(10, 6)) for i in range(coefs.shape[0]): plt.plot(alphas, coefs[i], label='Feature {}'.format(i+1)) plt.xscale('log') plt.xlabel('Log(alpha)') plt.ylabel('Coefficients') plt.title('Lasso Paths') plt.legend(loc='best') plt.show() # lambda与RMSE关系曲线图 mse_mean = np.mean(mse, axis=0) # 修改此处，使用axis=0计算均值 plt.figure(figsize=(10, 6)) plt.plot(alphas, mse_mean, label='Mean Squared Error') plt.axvline(alpha_opt, linestyle='--', color='r', label='Optimal Lambda') plt.xscale('log') plt.xlabel('Log(alpha)') plt.ylabel('Mean