# your code
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression, Ridge, Lasso
-
-# 将数据读取为 data frame 格式
-data = pd.DataFrame({
- 'X1': [7, 1, 11, 11, 7, 11, 3, 1, 2, 21, 1, 11, 10],
- 'X2': [26, 29, 56, 31, 52, 55, 71, 31, 54, 47, 40, 66, 68],
- 'X3': [6, 15, 8, 8, 6, 9, 17, 22, 18, 4, 23, 9, 8],
- 'X4': [60, 52, 20, 47, 33, 22, 6, 44, 22, 26, 34, 12, 12],
- 'Y': [78.5, 74.3, 104.3, 87.6, 95.9, 109.2, 102.7, 72.5, 93.1, 115.9, 83.8, 113.3, 109.4]
-})
-
-# 分离出自变量和因变量
-X = data[['X1', 'X2', 'X3', 'X4']]
-y = data[['Y']]
-
-# 将训练集和测试集按 8:2 分割，随机种子为学号后两位
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=34)
-
-# 线性回归模型
-linear_model = LinearRegression()
-linear_model.fit(X_train, y_train)
-print('线性回归：')
-print('w=', linear_model.coef_)
-print('b=', linear_model.intercept_)
-print('score=', linear_model.score(X_test, y_test))
-
-# 岭回归模型
-ridge_model = Ridge(alpha=1.0)
-ridge_model.fit(X_train, y_train)
-print('\n岭回归：')
-print('w=', ridge_model.coef_)
-print('b=', ridge_model.intercept_)
-print('score=', ridge_model.score(X_test, y_test))
-
-# LASSO 回归模型
-lasso_model = Lasso(alpha=1.0)
-lasso_model.fit(X_train, y_train)
-print('\nLASSO 回归：')
-print('w=', lasso_model.coef_)
-print('b=', lasso_model.intercept_)
-print('score=', lasso_model.score(X_test, y_test))
-
-