Helpful examples of using Elastic Net Regularization machine learning algorithms in scikit-learn.
The Elastic Net algorithm is a regularized regression method that combines the penalties of both L1 (Lasso) and L2 (Ridge) regularizations.
It aims to improve model accuracy and interpretability by addressing some of the limitations of Lasso and Ridge regression individually.
Elastic Net is particularly useful when dealing with high-dimensional data where the number of predictors exceeds the number of observations or when predictors are highly correlated.
The algorithm works by linearly combining the L1 and L2 penalties, controlled by two hyperparameters: the mixing parameter (alpha) and the regularization strength (lambda).
This combination allows Elastic Net to perform variable selection and shrinkage simultaneously, providing a balance between the sparsity of the model (as in Lasso) and the stability of coefficient estimates (as in Ridge). Consequently, Elastic Net is versatile and effective for creating predictive models with improved accuracy and generalizability.