What are the different regularization techniques in machine learning?

What are the different regularization techniques in machine learning?

Regularization techniques help prevent overfitting in machine learning models. Common methods include L1 (Lasso), L2 (Ridge), and Elastic Net.

Can you explain how L1 and L2 regularization differ?

L1 regularization adds the absolute value of coefficients to the loss function, promoting sparsity. L2 regularization adds the squared value, which tends to distribute weights more evenly.

When should I use Elastic Net over Lasso or Ridge?

Elastic Net is useful when you have many correlated features. It combines L1 and L2 penalties, balancing the benefits of both.

What are the advantages of using regularization?

Regularization improves model generalization, reduces overfitting, and can enhance interpretability by simplifying the model.