Elastic Net introduces two additional parameters, alpha (α) and lambda (λ), to the regression model. The parameter α controls the balance between Lasso and Ridge penalties, while λ determines the overall strength of regularization. When α is set to 0, Elastic Net behaves like Ridge regression, and when set to 1, it behaves like Lasso regression. Intermediate values of α provide a mix of both, offering a more flexible approach to model building.
