linregsk.py

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

diabetes=datasets.load_diabetes()

#print(diabetes)


#use only one feature
diabetes_X=diabetes.data[:,np.newaxis,2]
print(diabetes_X)

diabetes_X_train=diabetes_X[:-20]
diabetes_X_test=diabetes_X[-20:]

diabetes_y_train=diabetes.target[:-20]
diabetes_y_test=diabetes.target[-20:]

regr=linear_model.LinearRegression()

regr.fit(diabetes_X_train,diabetes_y_train)

diabetes_y_pred=regr.predict(diabetes_X_test)

print('Coefficients: \n',regr.coef_)

print("Mean Squared Error: %.2f" % mean_squared_error(diabetes_y_test,diabetes_y_pred))

print('Variance score : %.2f' % r2_score(diabetes_y_test,diabetes_y_pred))

plt.scatter(diabetes_X_test,diabetes_y_test,color='black')
plt.plot(diabetes_X_test,diabetes_y_pred,color='blue',linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()