What is the difference between L1 and L2 regularization?

What is the difference between L1 and L2 regularization?

L1 regularization adds the absolute value of the coefficients as a penalty term to the loss function, while L2 regularization adds the square of the coefficients. This leads to different effects on the model.

Can you explain how L1 regularization affects feature selection?

Sure! L1 regularization can shrink some coefficients to zero, effectively performing feature selection by removing less important features from the model.

What about L2 regularization? How does it impact the model?

L2 regularization tends to distribute the error among all features, which helps in reducing overfitting but does not eliminate any features completely.

When should I use L1 over L2 regularization?

Use L1 regularization when you suspect that many features are irrelevant and you want a sparse model. Use L2 when you believe all features are relevant and want to prevent overfitting.

Are there situations where I would use both?

Yes, using both L1 and L2 regularization together is known as Elastic Net, which can combine the benefits of both methods.