What are the advantages of using Elastic Net in regression analysis?

What are the advantages of using Elastic Net in regression analysis?

Elastic Net combines the strengths of both Lasso and Ridge regression, making it effective for high-dimensional data.

Can you explain how it handles multicollinearity?

Elastic Net is particularly useful in situations with multicollinearity as it can select groups of correlated variables.

What are some scenarios where Elastic Net is preferred over other methods?

It's preferred when you have many predictors, especially when some are highly correlated, or when you want variable selection.

Are there any limitations to using Elastic Net?

Yes, it requires careful tuning of parameters, and if not set correctly, it may not perform as well as other methods.

How does Elastic Net compare to Lasso and Ridge individually?

Elastic Net can outperform Lasso and Ridge by balancing their strengths, especially in complex datasets with many features.