Can you explain what Elastic Net is?
Elastic Net is a regularization technique that combines L1 and L2 penalties to improve model performance.
How does it differ from Lasso and Ridge regression?
While Lasso uses L1 penalty and Ridge uses L2, Elastic Net incorporates both, making it versatile for various datasets.
When should I use Elastic Net over Lasso or Ridge?
Use Elastic Net when you have highly correlated features or when you want to select a group of variables.
Can you give me an example of when Elastic Net is beneficial?
Sure! It's particularly useful in genomics where many predictors are correlated, helping to avoid overfitting.
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