How to do it…

Let's see how to compute regression accuracy in Python:

1. Now we will use the functions available to evaluate the performance of the linear regression model we developed in the previous recipe:

import sklearn.metrics as sm
print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2)) 
print("Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2)) 
print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2)) 
print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2)) 
print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2))

The following results are returned:

Mean absolute error = 241907.27
Mean squared error = 81974851872.13
Median absolute error = 240861.94
Explain variance score = 0.98
R2 score = 0.98

An R2 score near 1 means that the model is able to predict the data very well. Keeping track of every single metric can get tedious, so we pick one or two metrics to evaluate our model. A good practice is to make sure that the mean squared error is low and the explained variance score is high.