What are the different regularization methods in machine learning?

What are the different regularization methods in machine learning?

In machine learning, common regularization methods include L1 (Lasso), L2 (Ridge), and Elastic Net. Would you like to know how they differ?

Yes, how do L1 and L2 regularization differ?

L1 regularization adds the absolute value of coefficients as a penalty, promoting sparsity. L2 regularization adds the square of coefficients, which tends to distribute weights more evenly.

When should I use L1 over L2 regularization?

Use L1 regularization when you want feature selection and sparsity, while L2 is better for cases where you want to keep all features but reduce their impact.

Can you give an example of when to 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.