What is regularization in regression?

What is regularization in regression?

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

How does regularization help with overfitting?

It discourages overly complex models by penalizing large coefficients, leading to simpler models that generalize better.

What are the common types of regularization?

The most common types are Lasso (L1) and Ridge (L2) regularization, each applying different penalties.

When should I use Lasso over Ridge?

Use Lasso when you want feature selection, as it can shrink some coefficients to zero, while Ridge is better for multicollinearity.

Can I combine both Lasso and Ridge?

Yes, combining them is known as Elastic Net, which can leverage the benefits of both methods.