Elastic-net regression minimizes squared error plus both an coefficient penalty and a squared penalty. It combines exact sparsity with strict convexity when the quadratic penalty is positive.
The quadratic penalty encourages strongly correlated predictors to receive similar coefficients, so an elastic net tends to retain or discard correlated predictors as a group more readily than the Lasso.
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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.