When would you replace squared loss with Huber or quantile loss?
PICTURE THIS: 1, 2, 2, 8
Simple meaning
Squared loss explodes with outliers and estimates a mean.
WHY — Linear Regression instead of guessing?
Why interviewers care about Linear Regression:
on Linear Regression.
the situation, the default choice, and one exception - that reads as experience.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Squared loss explodes with
outliers and estimates a mean.
- 2Huber behaves like squares
near zero and like absolute error in the tails, which robustifies the fit.
- 3Quantile loss targets a
percentile such as median or a 90th-percentile demand forecast.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Squared loss explodes with outliers and estimates a mean. Huber behaves like squares near zero and like absolute error in the tails, which robustifies the fit.