What is Elastic Net Regularization and when should I use it?

What is Elastic Net Regularization and when should I use it?

Elastic Net Regularization is a technique that combines L1 and L2 regularization to improve model performance, especially in high-dimensional datasets.

Why would I choose Elastic Net over Lasso or Ridge?

You might choose Elastic Net when you have many correlated features, as it can select groups of variables together, unlike Lasso.

Can you give me an example of when to use it?

Sure! If you're working with a dataset where features are highly correlated, like in genomics, Elastic Net can help you select relevant features effectively.

How do I implement Elastic Net in Python?

You can use the ElasticNet class from the sklearn.linear_model module. Just import it and fit it to your data!