What is regularization in machine learning?

What is regularization in machine learning?

Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty to the loss function.

Can you explain the different types of regularization?

Sure! The two most common types are L1 regularization (Lasso) and L2 regularization (Ridge). L1 adds absolute value penalties, while L2 adds squared penalties.

When should I use L1 over L2 regularization?

Use L1 regularization when you want feature selection, as it can shrink some coefficients to zero. L2 is better for cases where you want to keep all features.

How do I implement regularization in Python?

You can use libraries like Scikit-learn. For example, you can use Lasso for L1 and Ridge for L2 regularization.