What are the different types of regularization in machine learning?

What are the different types of regularization in machine learning?

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

Can you explain L1 regularization in more detail?

L1 regularization adds a penalty equal to the absolute value of the coefficients. It can lead to sparse models, effectively selecting features.

What about L2 regularization?

L2 regularization adds a penalty equal to the square of the coefficients. It tends to distribute the error among all features, preventing any single feature from dominating.

When should I use Elastic Net?

Elastic Net is useful when you have many correlated features. It combines L1 and L2 penalties, balancing between feature selection and coefficient shrinkage.