What are the common loss functions used in regression?
Candidates must list and briefly explain loss functions like MSE, MAE, and Huber Loss suitable for regression tasks.
Why Interviewers Ask This
Loss functions drive the optimization process in machine learning. Interviewers ask this to verify you know which error metric to minimize for regression problems and understand the implications of each choice, such as sensitivity to outliers.
How to Answer This Question
List the primary regression loss functions: Mean Squared Error (MSE), Mean Absolute Error (MAE), and Huber Loss. Explain that MSE squares errors, making it sensitive to outliers but smooth. MAE takes absolute differences, making it robust to outliers. Mention Huber Loss as a compromise that behaves like MSE for small errors and MAE for large ones. Briefly touch upon R-squared as a metric rather than a loss function if relevant.
Key Points to Cover
- MSE is sensitive to outliers due to squaring.
- MAE is robust to outliers.
- Huber Loss combines properties of MSE and MAE.
- Choice depends on the presence of outliers in data.
Sample Answer
In regression, the most common loss function is Mean Squared Error (MSE), which penalizes larger errors heavily by squaring them, making it sensitive to outliers. Mean Absolute Error (MAE) is another option that uses absolute differences, offering greater robustness against outliers since it doesn't square the errors. For cases where we want a balance, Huber Loss is used; it acts like MSE for small residuals but switches to MAE for large ones, reducing the influence of extreme outliers while maintaining differentiability near zero.
Common Mistakes to Avoid
- Listing classification losses like Cross-Entropy.
- Confusing R-squared as a loss function.
- Not explaining the outlier sensitivity of MSE.
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