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