When should I use L2 regularization in machine learning?

When should I use L2 regularization in machine learning?

L2 regularization is useful when you want to prevent overfitting, especially in models with many features.

Can you explain how L2 regularization works?

Sure! L2 regularization adds a penalty equal to the square of the magnitude of coefficients to the loss function.

What are the benefits of using L2 over L1 regularization?

L2 regularization tends to produce smaller, more evenly distributed weights, while L1 can lead to sparse models.

Are there any situations where I shouldn't use L2 regularization?

Yes, if your model is already simple or if you need a sparse solution, L1 might be more appropriate.