Elastic Net

Elastic net is a regularization technique that combines LASSO and ridge regression and thereby represents a compromise between the advantages and disadvantages of the two algorithms.

Elastic net is commonly used in statistical modeling and machine learning to improve the prediction accuracy and interpretability of regression models by combining the strengths of LASSO (linear or “L1 penalty”) and ridge regression (quadratic or “L2 penalty”).

Elastic net works by adding both the L1 and L2 penalties to the loss function, which helps to enforce sparsity and reduce multicollinearity among the predictor variables. This results in a model that can handle highly correlated features and perform feature selection.

Unintuitively, it can be shown to be mathematically isomorphic to support vector machines, which makes implementing it considerably easier and more efficient.