How does regularization work in machine learning?

Can you explain how regularization works in machine learning?

Sure! Regularization is a technique used to prevent overfitting by adding a penalty to the loss function.

What are the different types of regularization?

The two most common types are L1 regularization (Lasso) and L2 regularization (Ridge). They add different penalties.

How do I choose between L1 and L2 regularization?

It depends on your data. L1 can lead to sparse models, while L2 tends to distribute weights more evenly.

Can regularization improve model performance?

Yes, by reducing overfitting, regularization can lead to better generalization on unseen data.