One way to keep a model’s overall complexity in check is to introduce a penalty parameter, in a step known as regularization. This new parameter penalizes any increase in a model’s complexity by artificially inflating prediction error, thus enabling the algorithm to account for both complexity and accuracy in optimizing its original parameters. By keeping a model simple, we help to maintain its generalizability.
