Python Machine Learning Blueprints - Second Edition

Evaluating the model

To illustrate why this is more useful, let's run the following scenario on two sample series:

a = pd.Series([10,10,10,10]) 
b = pd.Series([12,8,8,12]) 
 
np.sqrt(np.mean((b-a)**2))/np.mean(a) 

This generates the following output:

Now, compare that to the mean:

(b-a).mean() 

This generates the following output:

Clearly, the latter is the more meaningful statistic. Now, let's run it for our model:

np.sqrt(np.mean((y_pred-y_actual)**2))/np.mean(y_actual) 

This generates the following output:

Suddenly, our awesome model looks ...