When is Elastic Net better than pure Lasso or Ridge?
mediumAnswer
- Elastic Net combines L1 and L2 penalties: .
- Use it when (1) features are highly correlated — Lasso arbitrarily picks one and drops the rest, Elastic Net groups correlated features together via the L2 term; (2) you have more features than samples (p >> n) — Lasso caps the number of selected features at n.
- Tune rho on validation data.
Check yourself — multiple choice
- Elastic Net always equals Ridge
- It combines L1 and L2, handling correlated features and p >> n better than pure Lasso
- It only works for classification
- It replaces cross-validation
Elastic Net = L1 + L2. Better than Lasso when features are correlated or p >> n.
#regularization#linear-models
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