How do Lasso and Ridge regression differ in handling features?
This question requires an explanation of L1 vs L2 regularization, focusing on feature selection capabilities and weight manipulation.
Why Interviewers Ask This
Understanding the mathematical and practical differences between L1 and L2 regularization is essential for feature engineering and model tuning. Interviewers want to see if you know when to use each method based on the dataset's characteristics, such as the presence of irrelevant features or correlated variables.
How to Answer This Question
Explain that both add penalty terms to the loss function but differ in how they calculate the penalty. Highlight that L1 (Lasso) uses absolute values and can shrink weights to zero, effectively performing feature selection. Contrast this with L2 (Ridge), which squares the weights, reducing them but rarely eliminating them entirely. Conclude by mentioning Elastic Net as a hybrid approach.
Key Points to Cover
- L1 regularization performs automatic feature selection
- L2 regularization shrinks weights without eliminating them
- Lasso is preferred for sparse datasets
- Ridge handles correlated features better than Lasso alone
Sample Answer
Lasso (L1) and Ridge (L2) are both regularization techniques used to prevent overfitting, but they handle coefficients differently. Lasso adds the absolute value of weights to the loss function, which can shrink some wei…
Common Mistakes to Avoid
- Confusing which norm corresponds to L1 or L2
- Not mentioning feature selection capability of Lasso
- Forgetting to mention the formula difference
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