What are the benefits of L2 regularization in machine learning?

What are the benefits of L2 regularization in machine learning?

L2 regularization, also known as Ridge regression, helps prevent overfitting by adding a penalty for larger coefficients.

How does it compare to L1 regularization?

L1 regularization can lead to sparse models by driving some coefficients to zero, while L2 regularization keeps all features.

Can L2 regularization improve model performance?

Yes, it can improve generalization on unseen data by reducing model complexity.

Are there any downsides to using L2 regularization?

One downside is that it may not perform well if the true model is sparse, as it doesn't eliminate any features.

When should I use L2 regularization?

Use L2 regularization when you have many features and want to prevent overfitting without eliminating any variables.