Linear Regression Example

print(__doc__)
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[:,np.newaxis,2]

#Split the data into training/testing sets
diabetes_x_train = diabetes_x[:-20]
diabetes_x_test = diabetes_x[-20:]


# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

#Create linear regression object
regr = linear_model.LinearRegression()

#Train the model using the training sets
regr.fit(diabetes_x_train, diabetes_y_train)

#Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_x_test)

#The coefficients
print(‘Coefficients: \n‘, regr.coef_)

#The mean squared error
print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test,diabetes_y_pred))

# Explained variance score: 1 is perfect prediction
print(‘Variance score: %.2f‘ % r2_score(diabetes_y_test,diabetes_y_pred))

#Plot outputs
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()