diabetes多元线性回归

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score

diabetes = datasets.load_diabetes()

diabetes_x = diabetes.data
diabetes_y = diabetes.target

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(diabetes_x,diabetes_y,test_size=0.2)

regr = linear_model.LinearRegression() 

regr.fit(x_train,y_train)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
         normalize=False)

diabetes_y_pred = regr.predict(x_test)

print('cofficients:\n', regr.coef_)
print("variance score : %.2f" % r2_score(y_test,diabetes_y_pred))

cofficients:
 [ -18.68756731 -213.7914401   538.72386928  334.46539004 -701.88561869
  398.26399088   38.11345404  159.51521337  675.36903416   75.41306949]
variance score : 0.48

print('intercept:\n', regr.intercept_)

intercept:
 151.22099449096885