What is the difference between Lasso and Ridge regularization?

Can you explain the difference between Lasso and Ridge regularization?

Sure! Lasso (L1 regularization) adds a penalty equal to the absolute value of the coefficients, while Ridge (L2 regularization) adds a penalty equal to the square of the coefficients.

What are the main purposes of using these regularization techniques?

Both techniques are used to prevent overfitting in models by adding a penalty to the loss function, but they do so in different ways.

When should I use Lasso over Ridge?

Use Lasso when you want feature selection, as it can shrink some coefficients to zero. Ridge is better when you want to keep all features.

Can I use both Lasso and Ridge together?

Yes, you can use both in a technique called Elastic Net, which combines the penalties of both Lasso and Ridge.

What are some practical examples of using these techniques?

They are commonly used in linear regression models, especially in high-dimensional datasets where feature selection is crucial.