How does L2 regularization work?

Can you explain what L2 regularization is?

Sure! L2 regularization, also known as Ridge regression, adds a penalty equal to the square of the magnitude of coefficients to the loss function.

Why is L2 regularization important in machine learning?

It's important because it helps prevent overfitting by discouraging overly complex models, leading to better generalization.

How does it differ from L1 regularization?

L1 regularization adds a penalty equal to the absolute value of coefficients, which can lead to sparse models, while L2 keeps all features.

Can you give me an example of when to use L2 regularization?

You might use L2 regularization when you have many features that are correlated, as it helps to stabilize the estimates.