How does L1 regularization work?

Can you explain how L1 regularization works in machine learning?

Sure! L1 regularization, also known as Lasso regularization, adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. This helps in feature selection by shrinking some coefficients to zero.

Why is it important to use L1 regularization?

It's important because it helps prevent overfitting by reducing the complexity of the model. By eliminating less important features, it can improve model interpretability and performance.

How does it compare to L2 regularization?

L1 regularization can produce sparse models by setting some coefficients to zero, while L2 regularization shrinks all coefficients but does not set any to zero. This means L1 can be better for feature selection.

Can you give an example of when to use L1 regularization?

You might use L1 regularization when you have a high-dimensional dataset with many features, and you suspect that only a few of them are actually important for prediction.