What are the differences between Elastic Net, Lasso, and Ridge regression?

What are the differences between Elastic Net, Lasso, and Ridge regression?

Elastic Net, Lasso, and Ridge are regularization techniques used in regression analysis to prevent overfitting. Lasso adds an L1 penalty, Ridge adds an L2 penalty, and Elastic Net combines both.

Can you explain how Lasso regression works?

Lasso regression works by adding a penalty equal to the absolute value of the magnitude of coefficients. This can shrink some coefficients to zero, effectively performing variable selection.

When should I use Elastic Net instead of Lasso or Ridge?

Use Elastic Net when you have many correlated features. It combines the strengths of both Lasso and Ridge, providing better performance in such cases.

What are the advantages of using Ridge regression?

Ridge regression is particularly useful when dealing with multicollinearity, as it can handle correlated predictors without eliminating any variables.

Can you give me an example of when to use these techniques?

Sure! If you have a dataset with many features and suspect some are irrelevant, Lasso can help. If features are correlated, Elastic Net is a good choice. Ridge is best when you want to keep all features but reduce their impact.