AI Engineering Degree Practice Exam 2025 – All-in-One Guide to Master Your Degree!

Regularization serves a crucial function in machine learning by addressing the issue of overfitting, which occurs when a model learns not just the underlying patterns in the training data but also the noise. This can lead to a model that performs well on training data but poorly on new, unseen data.

The fundamental goal of regularization is to apply a penalty to the model for having excessively large weights. By discouraging large weights, regularization helps to simplify the model, effectively making it more generalizable. This generalization is essential because it allows the model to perform better when faced with new data, thereby enhancing its predictive performance.

There are different forms of regularization, such as L1 and L2 regularization. L1 regularization, known as Lasso, can lead to sparse models, effectively eliminating some feature weights entirely. L2 regularization, known as Ridge, shrinks the weights but typically does not eliminate them. Both methods play pivotal roles in controlling model complexity by managing the weight values, thus reinforcing the idea that simpler models tend to generalize better.

In summary, the purpose of regularization is to prevent overfitting by penalizing large weights, promoting the creation of models that are robust and capable of performing well on various datasets,