How do Lasso and Ridge regularization differ in practice?
Direct Answer
Candidates need to compare L1 and L2 penalties, specifically focusing on their impact on feature selection and weight distribution.
Why Interviewers Ask This
Regularization is a standard technique, but knowing the nuances between L1 (Lasso) and L2 (Ridge) shows advanced understanding. Interviewers ask this to determine if you understand which method to choose based on your data characteristics, such as the presence of correlated features or the need for feature selection.
How to Answer This Question
Start by defining both as methods to penalize large weights to prevent overfitting. Clearly state that Lasso (L1) uses absolute values and can shrink weights to exactly zero, effectively performing feature selection. Contrast this with Ridge (L2), which uses squared values and shrinks weights but rarely eliminates them entirely. Conclude by suggesting when to use each: Lasso for sparse data with irrelevant features, and Ridge when all features are likely useful.
Key Points to Cover
- Lasso (L1) performs feature selection by zeroing out weights.
- Ridge (L2) shrinks weights but retains all features.
- Lasso is better for sparse data; Ridge for correlated features.
- Both aim to reduce model complexity and prevent overfitting.
Sample Answer
Both Lasso and Ridge regularization add a penalty term to the loss function to discourage large weights, but they differ in their mathematical approach. Lasso (L1) adds the absolute value of weights, which can shrink som…
Common Mistakes to Avoid
- Claiming Ridge removes features completely.
- Confusing the penalty formulas (absolute vs squared).
- Failing to mention the use case for each method.
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