Elastic net regularization is a technique used in statistical models to improve their accuracy and interpretability by combining the properties of two other regularization methods: Lasso (L1) and Ridge (L2) regression. It is particularly useful for handling high-dimensional data where the number of predictors (features) can be much larger than the number of observations (samples). This is common in fields like genomics and cancer research.
