from sklearn import datasets # 数据集
from sklearn.model_selection import train_test_split
 linear_model
import matplotlib.pyplot as plt 

boston = datasets.load_boston()  波士顿房价数据
boston

 创建训练集 与 测试集
x_train,x_test,y_train,y_test = train_test_split(boston.data,boston.target,test_size=0.1,random_state=42)
print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)

 # 训练数据
 linreg = linear_model.LinearRegression()
 linreg.fit(x_train,y_train)

 # 得出预测值

 y_pred = linreg.predict(x_test)
 y_pred

plt.figure(figsize=(10,6))  设置大小

plt.plot(y_test,linewidth=3,label='Actual') 
plt.plot(y_pred,1)">Prediction)

 显示上面设置的名字与底部
plt.legend(loc=best)
plt.xlabel(test data point)
plt.ylabel(target value')

plt.plot(y_test,y_pred,'o')
plt.plot([-10,60],[-10,'k--')
plt.axis([-10,60,-10,60])

plt.xlabel('Actual')
plt.ylabel('Prediction')