What are the differences between L1 and L2 regularization?

What are the differences between L1 and L2 regularization?

L1 regularization adds the absolute value of the coefficients as a penalty term, 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?

L1 regularization can lead to sparse models, effectively reducing the number of features by driving some coefficients to zero, which aids in feature selection.

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

L2 regularization tends to shrink the coefficients but does not set them to zero, which means it retains all features but reduces their impact.

When should I use L1 over L2 regularization?

Use L1 regularization when you suspect that many features are irrelevant and you want a simpler model. Use L2 when you want to keep all features but control for overfitting.

Are there situations where I would use both L1 and L2?

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