np.random.seed(42) q=np.array(X1[:2928]) w=np.array(x2[:2928]) e=np.array(x3[:2928]) r=np.array(x4[:2928]) t=np.array(x5[:2928]) p=np.array(x6[:2928]) u=np.array(x7[:2928]) eps=np.random.normal(0,0.05,152) X=np.c_[q,w,e,r,t,p,u] beta=[0.1,0.15,0.2,0.5,0.33,0.45,0.6] y=np.dot(X,beta) ''' X_model=sm.add_constant(X) model=sm.OLS(y,X_model) results=model.fit() print(results.summary()) ''' X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) alpha = 0.1 # 设置岭回归的惩罚参数 ridge = Ridge(alpha=alpha) ridge.fit(X_train, y_train) y_pred = ridge.predict(X_test) mse = mean_squared_error(y_test, y_pred) print('MSE:', mse)那这个代码要怎么修改才可以经过领回归之后再求出参数呢

如果您想使用岭回归（Ridge Regression）求解多元线性回归的参数，可以按照以下步骤修改代码：

1. 导入必要的库：

import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

2. 准备数据：

np.random.seed(42)
q = np.array(X1[:2928])
w = np.array(x2[:2928])
e = np.array(x3[:2928])
r = np.array(x4[:2928])
t = np.array(x5[:2928])
p = np.array(x6[:2928])
u = np.array(x7[:2928])
X = np.c_[q, w, e, r, t, p, u]
beta = [0.1, 0.15, 0.2, 0.5, 0.33, 0.45, 0.6]
y = np.dot(X, beta)

3. 划分数据集为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

4. 使用岭回归拟合数据：

alpha = 0.1  # 设置岭回归的惩罚参数
ridge = Ridge(alpha=alpha)
ridge.fit(X_train, y_train)

5. 计算岭回归的拟合效果：

y_pred = ridge.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print('MSE:', mse)

6. 计算多元线性回归的参数：

coef = ridge.coef_  # 计算岭回归的系数
intercept = ridge.intercept_  # 计算岭回归的截距
print('Coefficients:', coef)
print('Intercept:', intercept)

这样就可以使用岭回归求解多元线性回归的参数了。