Model Calibration Quant Interview Guide
Model calibration quant interview guide for probability forecasts, reliability curves, scoring rules, thresholds, examples, and failure modes.
Candidates discussing classification probabilities, risk forecasts, or decision thresholds.
Calibration is about probability meaning
A calibrated model assigns probabilities that match observed frequencies. If events forecast at 70 percent happen about 70 percent of the time, calibration is good.
Calibration differs from ranking
A model can rank cases well while producing poorly calibrated probabilities. Interviews often reward naming that distinction clearly.
Concrete example
If a model flags trades as profitable with 80 percent probability, check whether similar forecasts actually win near 80 percent after costs and timing rules.
Use proper evidence
Reliability curves, Brier score, log loss, and calibration by bucket can diagnose probability quality. Threshold decisions should include costs.
Common mistakes
Candidates often confuse higher confidence with better calibration. A confident wrong model is worse than a cautious model that reflects uncertainty.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.