The 60-second answer
Start with false-positive/false-negative costs and use precision, recall, PR-AUC, calibrated probabilities, or cost-weighted utility rather than accuracy alone. Use stratified/time-aware splits and inspect per-class and slice-level performance.
Build the answer in this order
Start with false-positive/false-negative costs and use precision, recall, PR-AUC, calibrated probabilities, or cost-weighted utility rather than accuracy alone.
Use stratified/time-aware splits and inspect per-class and slice-level performance.
Compare class weighting, focal loss, under/oversampling, hard-negative mining, and threshold tuning.
Select the operating threshold on validation data and monitor production prevalence because base-rate shift changes precision and calibration.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Model operational capacity explicitly: a threshold that maximizes recall may create an unsustainable review/alert queue.
- Recalibrate or adjust thresholds when class priors shift instead of blindly retraining.
What the interviewer is really testing
Likely follow-up questions
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.