What are L1 and L2 regularization techniques in machine learning?

What are L1 and L2 regularization techniques in machine learning?

L1 and L2 regularization are techniques used to prevent overfitting in machine learning models by adding a penalty to the loss function.

Can you explain how L1 regularization works?

L1 regularization adds the absolute value of the coefficients as a penalty term to the loss function, promoting sparsity.

What about L2 regularization?

L2 regularization adds the squared value of the coefficients as a penalty, which helps to reduce the model complexity without promoting sparsity.

When should I use L1 over L2 regularization?

Use L1 when you want feature selection and sparsity, and L2 when you want to keep all features but reduce their impact.