What are the different types of regularization techniques in machine learning?

What are the different types of regularization techniques in machine learning?

Regularization techniques help prevent overfitting in machine learning models. The main types include L1 (Lasso), L2 (Ridge), and Elastic Net.

Can you explain Lasso regularization in more detail?

Lasso regularization adds a penalty equal to the absolute value of the magnitude of coefficients. It can shrink some coefficients to zero, effectively performing variable selection.

What about Ridge regularization? How does it differ from Lasso?

Ridge regularization adds a penalty equal to the square of the magnitude of coefficients. Unlike Lasso, it does not shrink coefficients to zero but rather reduces their size.

What is Elastic Net and when should I use it?

Elastic Net combines both L1 and L2 penalties. It's useful when you have many correlated features, as it can select groups of variables.