Regularization Theory and Practice
Regularization is a technique that is almost always utilized in real-world applications of ML so it’s worth taking a closer look at it (after all, it’s in the title of this chapter so it must be worth exploring in depth)! Regularization is an important technique used to prevent overfitting and improve the generalization of models, or, how well they can perform in nuanced datasets beyond those they were trained on.
It involves adding a penalty term to the loss function (the method we use to evaluate our model’s performance) during the training process, which discourages the model from becoming too complex or relying too heavily on specific features. By doing so, regularization helps the model to capture the underlying patterns in the data rather than memorizing noise or peculiarities of the training set.
The main idea behind regularization is to strike a balance between model complexity and goodness of fit. Without regularization...
