What are some common regularization techniques in machine learning?

What are some common regularization techniques in machine learning?

Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net.

How does L1 regularization differ from L2 regularization?

L1 regularization adds the absolute value of coefficients to the loss function, promoting sparsity, while L2 adds the square of coefficients, which tends to shrink them.

When should I use Elastic Net?

Elastic Net is useful when you have many correlated features, as it combines the benefits of both L1 and L2 regularization.

Can regularization help with overfitting?

Yes, regularization helps prevent overfitting by penalizing large coefficients, leading to simpler models.

What are the hyperparameters for these techniques?

L1 and L2 regularization have a hyperparameter, often denoted as lambda (λ), which controls the strength of the penalty.