What is Elastic Net and when should it be used?
Direct Answer
This question explores the combination of L1 and L2 regularization and its specific advantages over individual methods.
Why Interviewers Ask This
Elastic Net represents a sophisticated understanding of regularization techniques. Interviewers ask this to see if you can handle situations where neither pure Lasso nor pure Ridge is sufficient, particularly when dealing with groups of correlated features.
How to Answer This Question
Define Elastic Net as a hybrid regularization method that combines L1 (Lasso) and L2 (Ridge) penalties. Explain that it balances feature selection (from Lasso) with weight reduction (from Ridge). Highlight its primary advantage: it works well when there are multiple correlated features, a scenario where Lasso might arbitrarily pick one and discard the rest, whereas Elastic Net tends to keep them together.
Key Points to Cover
- Combines L1 and L2 penalties.
- Balances feature selection and weight reduction.
- Effective for datasets with correlated features.
- Avoids Lasso's limitation of picking only one feature from a group.
Sample Answer
Elastic Net is a regularization technique that combines the penalties of both Lasso (L1) and Ridge (L2) regression. By mixing these two approaches, it benefits from Lasso's ability to perform feature selection and Ridge'…
Common Mistakes to Avoid
- Describing it as just another name for Ridge.
- Failing to mention the benefit with correlated features.
- Not explaining the combined penalty structure.
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