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 prevent overfitting by adding a penalty for larger coefficients. This can lead to simpler models that generalize better to unseen data.

Can you explain how it differs from L2 regularization?

Sure! L1 regularization can shrink some coefficients to zero, effectively performing feature selection, while L2 regularization shrinks coefficients but does not set them to zero.

What types of models benefit the most from L1 regularization?

Models like linear regression and logistic regression benefit significantly from L1 regularization, especially when dealing with high-dimensional data.

Are there any drawbacks to using L1 regularization?

Yes, one drawback is that it can lead to unstable estimates when features are highly correlated, as it may arbitrarily select one feature over another.

How do I implement L1 regularization in Python?

You can use libraries like Scikit-learn, where you can set the regularization parameter in models like Lasso for L1 regularization.