Elastic Net Regularization
Elastic Net Regularization is a regularization technique used in linear and logistic regression to prevent overfitting and improve the accuracy of the model. It is a combination of L1 and L2 regularization techniques. Elastic Net adds a penalty term to the loss function of the model, which is a combination of the L1 and L2 norms of the coefficients. The L1 norm encourages sparsity in the coefficients, while the L2 norm encourages small and non-zero coefficients. Elastic Net allows for feature selection and can handle highly correlated features. It is particularly useful when the number of features is larger than the number of observations, and when there are multiple correlated predictors. The strength of the regularization can be controlled by a hyperparameter alpha, which determines the balance between the L1 and L2 penalties.
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