Elastic Net Regression

Elastic Net regression is a regularization technique that combines both L1 and L2 penalties. By leveraging the benefits of both Lasso and Ridge, it can handle situations where there are many correlated features while still performing feature selection.

This is particularly useful in cryptocurrency markets where various indicators often show high levels of multicollinearity. Elastic Net provides a more flexible approach than using either method alone, allowing for better performance and more stable feature selection.

It is a powerful tool for quantitative researchers who need to build models that are both sparse and robust. It effectively manages the best of both worlds in regularization.