Can you give examples of L1 and L2 regularization?

What are L1 and L2 regularization in machine learning?

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.

Can you give me an example of L1 regularization?

Sure! In L1 regularization, if you have a linear regression model, the penalty term would be the sum of the absolute values of the coefficients multiplied by a regularization parameter.

And what about L2 regularization?

In L2 regularization, the penalty term is the sum of the squares of the coefficients. This helps to prevent overfitting by keeping the coefficients small.

How do I choose between L1 and L2 regularization?

It depends on your data. Use L1 if you want feature selection, as it can shrink some coefficients to zero. Use L2 if you want to keep all features but reduce their impact.