What are regularization methods in machine learning?

What are regularization methods in machine learning?

Regularization methods are techniques used to prevent overfitting in machine learning models by adding a penalty to the loss function.

Can you explain the difference between L1 and L2 regularization?

L1 regularization adds the absolute value of the coefficients as a penalty, while L2 regularization adds the square of the coefficients.

When should I use L1 regularization over L2?

Use L1 regularization when you want feature selection, as it can shrink some coefficients to zero. L2 is better for generalization.

Are there any other regularization techniques I should know about?

Yes, there are techniques like Elastic Net, which combines L1 and L2 regularization, and Dropout, commonly used in neural networks.