How does Lasso regularization work?

Can you explain how Lasso regularization works in machine learning?

Sure! Lasso regularization adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function.

What is the purpose of adding this penalty?

The penalty helps to reduce overfitting by shrinking some coefficients to zero, effectively performing variable selection.

How does it compare to Ridge regularization?

Unlike Ridge, which uses the square of the coefficients, Lasso can eliminate some features entirely by setting their coefficients to zero.

Can you give an example of when to use Lasso?

Lasso is particularly useful when you have a large number of features and want to identify the most important ones.