Elastic Net es un regularization technique used in regresión lineal que combina las propiedades de Lasso (Regularización L1) y Ridge (Regularización L2) regression. It is particularly useful when dealing with datasets that have many features, especially when some features are correlated.
Lasso regression can select a subset of features by forcing some coefficients to be exactly zero, which makes it useful for selección de características. However, when features are highly correlated, Lasso may arbitrarily select one feature over others. On the other hand, Ridge regression tends to include all features by shrinking the coefficients but does not perform feature selection.
Elastic Net addresses these issues by balancing the two approaches. It penalizes the size of the coefficients while also allowing for some coefficients to be zero, thereby performing both regularization and feature selection. The method introduces two parameters: alpha, which controls the overall strength of the penalty, and the mixing parameter (often denoted as lambda), which determines the balance between Lasso and Ridge penalties.
Matemáticamente, Elastic Net función de pérdida ||y – Xβ||² + λ ∑ ||β_g||_2
Loss = ||y – Xβ||² + α * (λ * ||β||² + (1 – λ) * ||β||₁)
Donde ||y – Xβ||² is the residual sum of squares, ||β||² is the L2 norm (Ridge penalty), and ||β||₁ es la norma L1 (penalización Lasso).
Elastic Net is widely used in various fields, including genomics and finance, where datasets often contain many correlated variables. By effectively managing multicollinearity and mejorar la interpretabilidad del modelo, Elastic Net helps in creating robust predictive models.