Why is Mean Squared Error sensitive to outliers?
This question tests your understanding of the mathematical properties of loss functions and their behavior in the presence of anomalous data points.
Why Interviewers Ask This
Outliers can skew model training significantly. Interviewers ask this to see if you understand why squaring errors amplifies their impact. They want to know if you can choose appropriate metrics like MAE or Huber when outliers are present.
How to Answer This Question
Explain that MSE squares the difference between predicted and actual values. This squaring operation disproportionately increases the penalty for large errors caused by outliers. Consequently, the model tries hard to fit these outliers, potentially degrading performance on the majority of the data. Suggest alternatives like MAE or Huber loss for robustness.
Key Points to Cover
- Squaring errors amplifies the impact of outliers.
- Large errors dominate the loss calculation.
- Can lead to poor generalization on non-outlier data.
- Alternatives like MAE are more robust.
Sample Answer
Mean Squared Error (MSE) calculates the average of the squared differences between predictions and actual values. Because the errors are squared, larger deviations (outliers) contribute significantly more to the total lo…
Common Mistakes to Avoid
- Saying MSE ignores outliers.
- Not explaining the mathematical reason (squaring).
- Failing to suggest robust alternatives.
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