What are the benefits of L1 regularization in machine learning?

What are the benefits of L1 regularization in machine learning?

L1 regularization, also known as Lasso regularization, helps in feature selection by shrinking some coefficients to zero, which simplifies the model.

How does it compare to L2 regularization?

L2 regularization, or Ridge regularization, penalizes the sum of the squares of the coefficients, which tends to keep all features but reduces their impact. L1 can eliminate some features entirely.

Can L1 regularization improve model interpretability?

Yes, by reducing the number of features, L1 regularization makes the model easier to interpret, as it highlights the most important predictors.

Are there any drawbacks to using L1 regularization?

One drawback is that it can be sensitive to outliers, which may lead to less stable estimates compared to L2 regularization.

When should I use L1 regularization over L2?

Use L1 regularization when you suspect that many features are irrelevant and you want a simpler, more interpretable model.