What is Elastic Net and when should you use it?
This question assesses your knowledge of hybrid regularization methods and your ability to select the right tool for correlated feature sets.
Why Interviewers Ask This
Elastic Net combines the strengths of Lasso and Ridge, addressing limitations of both. Interviewers ask this to see if you can handle complex scenarios where features are highly correlated. It demonstrates advanced understanding of regularization strategies beyond basic textbook definitions.
How to Answer This Question
Define Elastic Net as a linear combination of L1 and L2 penalties. Explain that it balances feature selection (from Lasso) and weight reduction (from Ridge). Highlight its primary advantage: handling groups of correlated features better than Lasso alone, which tends to pick only one variable from a correlated group.
Key Points to Cover
- Combines L1 and L2 penalties.
- Handles correlated features better than Lasso.
- Provides a balance between sparsity and coefficient stability.
- Useful when features are grouped and correlated.
Sample Answer
Elastic Net is a regularization technique that combines both L1 (Lasso) and L2 (Ridge) penalties into a single loss function. It balances the benefits of feature selection provided by Lasso with the stability of weight reduction offered by Ridge. I recommend using Elastic Net when dealing with datasets containing multiple correlated features. Unlike Lasso, which might arbitrarily select one feature from a correlated group, Elastic Net tends to keep or drop them together, providing a more robust model in such scenarios.
Common Mistakes to Avoid
- Thinking it is just an average of Lasso and Ridge.
- Forgetting to mention correlation handling.
- Not explaining the combined penalty structure.
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