Lasso and Elastic Net Regressions, Explained: A Visual Guide with Code Examples

Lasso and Elastic Net regressions are advanced variations of linear regression. Lasso automatically selects significant features by applying a penalty that can reduce some coefficients to zero, making it useful for feature selection. Elastic Net combines the traits of both Lasso and Ridge regressions, utilizing penalties to manage feature selection and correlation. Both use the coordinate descent algorithm for optimization, which updates coefficients iteratively. Practical code examples using Python's scikit-learn library demonstrate the implementation and training of these models.