Python Machine Learning Cookbook

Computing regression accuracy

Now that we know how to build a regressor, it's important to understand how to evaluate the quality of a regressor as well. In this context, an error is defined as the difference between the actual value and the value that is predicted by the regressor.

Getting ready

Let's quickly understand what metrics can be used to measure the quality of a regressor. A regressor can be evaluated using many different metrics, such as the following:

• Mean absolute error: This is the average of absolute errors of all the datapoints in the given dataset.
• Mean squared error: This is the average of the squares of the errors of all the datapoints in the given dataset. It is one of the most popular metrics out there!
• Median absolute error