When should I use Lasso regression instead of Ridge regression?

When should I use Lasso regression instead of Ridge regression?

Lasso regression is ideal when you want to perform feature selection, as it can shrink some coefficients to zero. Ridge regression, on the other hand, is better when you want to keep all features but reduce their impact.

Can you explain why Lasso can shrink coefficients to zero?

Lasso uses L1 regularization, which adds a penalty equal to the absolute value of the coefficients. This can lead to some coefficients being exactly zero, effectively selecting a simpler model.

What about Ridge regression? How does it differ?

Ridge regression uses L2 regularization, which adds a penalty equal to the square of the coefficients. This method shrinks coefficients but does not set them to zero, keeping all features in the model.

In what scenarios would I prefer Ridge over Lasso?

You'd prefer Ridge when you have many features that are correlated and you want to retain all of them, as it tends to perform better in such cases.

Are there situations where I should use both?

Yes, using Elastic Net combines both Lasso and Ridge penalties, which can be beneficial when you have a mix of correlated and uncorrelated features.