Explain the differences between Lasso and Ridge regression.
This question requires a detailed comparison of two popular regularization techniques and their impact on model coefficients and feature selection.
Why Interviewers Ask This
Lasso and Ridge are standard tools, but knowing when to use which shows deeper expertise. Interviewers ask this to test your understanding of how L1 and L2 penalties mathematically affect coefficients. They also want to see if you understand the implications for feature selection versus weight reduction.
How to Answer This Question
Define Lasso (L1) as adding absolute value penalties, which can shrink some weights to zero, effectively performing feature selection. Define Ridge (L2) as adding squared penalties, which shrinks weights but rarely eliminates them entirely. Contrast their use cases: Lasso for sparse models with many irrelevant features, and Ridge for models where all features are likely useful.
Key Points to Cover
- Lasso performs feature selection by setting weights to zero.
- Ridge reduces weights but retains all features.
- Lasso is ideal for high-dimensional sparse data.
- Ridge is better when all features are correlated and useful.
Sample Answer
Lasso regression uses L1 regularization, which adds the absolute value of weights to the loss function. This property allows it to shrink some coefficients to exactly zero, effectively performing automatic feature select…
Common Mistakes to Avoid
- Confusing the penalty formulas (absolute vs squared).
- Stating Ridge performs feature selection.
- Not explaining the scenario where each is preferred.
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