How do Lasso and Ridge regularization differ in feature selection?
This question probes deep understanding of penalty functions and their specific impact on model coefficients and feature elimination.
Why Interviewers Ask This
Understanding the mathematical nuances of regularization is key for feature engineering and model tuning. Interviewers want to see if you know that Lasso can set weights to zero for feature selection, while Ridge only shrinks them. This distinction determines which method to choose based on whether you need to eliminate irrelevant features or simply control complexity.
How to Answer This Question
Explain that both methods add a penalty term to the loss function to prevent overfitting. Highlight 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 reduces weights without eliminating them. Mention Elastic Net as a hybrid approach for correlated features.
Key Points to Cover
- Lasso performs feature selection by setting weights to zero.
- Ridge shrinks weights but retains all features.
- Elastic Net combines both L1 and L2 penalties.
- Choice depends on feature relevance and correlation.
Sample Answer
Lasso (L1) and Ridge (L2) both regularize models to prevent overfitting but differ in their penalty mechanisms. Lasso adds the absolute value of weights to the loss function, which can shrink some coefficients to exactly zero, thus performing automatic feature selection. Ridge adds the squared value of weights, which reduces large weights but keeps all features in the model. Therefore, Lasso is ideal when many features are irrelevant, while Ridge is better when all features contribute but need scaling.
Common Mistakes to Avoid
- Claiming Ridge eliminates features.
- Not mentioning the geometric interpretation of penalties.
- Failing to explain why one might be preferred over the other.
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