sklearn简单线性回归

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

# 加载数据，波士顿房价
boston = datasets.load_boston()
x, y = boston.data, boston.target

# 划分训练集和检验集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=10010)

# 使用训练集训练模型
reg = LinearRegression()
reg.fit(x_train, y_train)

# 使用模型进行预测
y_predict = reg.predict(x_test)

# 计算模型的预测值与真实值之间的均方误差MSE
print(mean_squared_error(y_test, y_predict))