How do Elastic Net, Lasso, and Ridge regression compare?

Can you explain the differences between Elastic Net, Lasso, and Ridge regression?

Sure! Lasso regression adds an L1 penalty, which can shrink some coefficients to zero, effectively performing variable selection. Ridge regression adds an L2 penalty, which shrinks coefficients but doesn't set them to zero. Elastic Net combines both penalties, allowing for a balance between variable selection and coefficient shrinkage.

When should I use Elastic Net over Lasso or Ridge?

You should consider using Elastic Net when you have a large number of features, especially when they are correlated. It can handle situations where Lasso might select one variable and ignore others that are correlated, while Ridge might include all variables but with smaller coefficients.

What are the advantages of using Lasso regression?

Lasso regression is great for feature selection because it can reduce the number of variables in your model by setting some coefficients to zero. This can lead to simpler models that are easier to interpret and can help prevent overfitting.

Can you give me an example of when Ridge regression is preferred?

Ridge regression is preferred when you have multicollinearity in your data. It helps to stabilize the estimates by adding a penalty to the size of the coefficients, which can lead to better predictions when the predictors are highly correlated.