Elastic Net Regularization

Elastic Net is a regularization technique that combines the penalties of both L1 (Lasso) and L2 (Ridge) regularization methods. It aims to improve model performance by addressing limitations of each individual method, particularly in situations where there are many correlated features. By using a linear combination of L1 and L2 penalties, Elastic Net can perform feature selection (like Lasso) and handle multicollinearity (like Ridge) simultaneously.