 Elastic net regularization

Elastic-net regression minimizes squared error plus both an ${\ell }^1$ coefficient penalty and a squared ${\ell }^2$ penalty. It combines exact sparsity with strict convexity when the quadratic penalty is positive.

Elastic Net regularization is a machine learning technique used to enhance the performance of linear regression models by addressing the problems of multicollinearity and overfitting. It combines two types of regularization techniques: Lasso (L1) and Ridge (L2) regularization. ### Key Components: 1. **Lasso Regularization (L1)**: - Adds a penalty equal to the absolute value of the coefficients (weights) to the loss function.