代码：
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection as modsel
from sklearn import linear_model
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use(\'ggplot\')
boston = datasets.load_boston()
print(dir(boston))
print(boston.data.shape)
print(boston.target.shape)
x_train,x_test,y_train,y_test = modsel.train_test_split(boston.data,boston.target,test_size=0.1,random_state=42)
linreg = linear_model.LinearRegression()
linreg.fit(x_train, y_train)
# linreg.predict(x_train) 是预测值
print(\'预测值的方差：\t\' + str(metrics.mean_squared_error(y_train, linreg.predict(x_train))))
print(\'预测值的确定系数（R方值）：\t\' + str(linreg.score(x_train, y_train)))
y_pred = linreg.predict(x_test)
metrics.mean_squared_error(y_test,y_pred)
plt.figure(figsize=(10,6))
plt.plot(y_test,linewidth=3,label=\'ground truth\')
plt.plot(y_pred,linewidth=3,label=\'predicted\')
plt.legend(loc=\'best\')
plt.xlabel(\'test data point\')
plt.ylabel(\'target value\')
plt.plot(y_test,y_pred,\'o\')
plt.plot([-10,60],[-10,60],\'k--\')
plt.axis([-10,60,-10,60])
plt.xlabel(\'ground truth\')
plt.ylabel(\'predicted\')
scorestr = \'R$^2$=%.3f\'%linreg.score(x_test,y_test)
errstr = \'MSE = %.3f\' %metrics.mean_squared_error(y_test,y_pred)
plt.text(-5,50,scorestr,fontsize=12)
plt.text(-5,45,errstr,fontsize=12)