from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# 加載數據，波士頓房價
boston = datasets.load_boston()
x, y = boston.data, boston.target
# 划分訓練集和檢驗集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=10010)
# 使用訓練集訓練模型
reg = LinearRegression()
reg.fit(x_train, y_train)
# 使用模型進行預測
y_predict = reg.predict(x_test)
# 計算模型的預測值與真實值之間的均方誤差MSE
print(mean_squared_error(y_test, y_predict))
