What are the types of regularization?

What are the types of regularization used in machine learning?

There are several types of regularization techniques, including L1 regularization, L2 regularization, and Elastic Net. Would you like to know more about each?

Yes, please explain L1 and L2 regularization.

L1 regularization, also known as Lasso, adds a penalty equal to the absolute value of the magnitude of coefficients. L2 regularization, or Ridge, adds a penalty equal to the square of the magnitude of coefficients. Both help prevent overfitting.

How do I choose between L1 and L2 regularization?

Choosing between L1 and L2 depends on your data. L1 can lead to sparse models, while L2 tends to distribute weights more evenly. If feature selection is important, L1 might be better.

Can I use both types of regularization?

Yes, you can use both types together in a technique called Elastic Net, which combines the benefits of both L1 and L2 regularization.