The 60-second answer
Start from the error costs and class prevalence before choosing a metric. Precision measures correctness among predicted positives; recall measures coverage of actual positives.
Build the answer in this order
1
Give the core idea
Start from the error costs and class prevalence before choosing a metric.
2
Explain how it works
Precision measures correctness among predicted positives; recall measures coverage of actual positives.
3
Compare alternatives
Use PR-AUC for rare positive classes and ROC-AUC for threshold-independent ranking when prevalence is less extreme.
4
State failure modes + validation
Choose an operating threshold using business cost, capacity, or safety constraints.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
01Definition
02Mechanism
03Trade-offs
04Failure modes
05When to use
Senior-level signal
- Check calibration and slice metrics in addition to aggregate discrimination.
- Account for prevalence drift because precision can change even when the ranker is unchanged.
What the interviewer is really testing
Mechanistic understanding, assumptions, trade-offs, and whether you can turn a definition into a model decision.
Likely follow-up questions
What assumption makes this approach work?
When would you choose the strongest alternative instead?
What production or data failure mode changes your answer?
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.