ROC AUC Quant Interview Guide
ROC AUC quant interview guide for threshold sweeps, true positive rate, false positive rate, ranking intuition, limitations, and examples.
Candidates discussing ranking quality and classification thresholds.
ROC curves sweep thresholds
A ROC curve plots true positive rate against false positive rate across thresholds. It shows ranking behavior across many decision cutoffs instead of one fixed threshold.
AUC summarizes ranking quality
AUC is often interpreted as how well the model ranks positives above negatives. It can be useful, but it does not directly encode payoff, calibration, or capacity constraints.
Concrete example
A model can have strong AUC but still be poor for a strategy if the actionable top slice is weak after costs. Ranking quality must connect to the actual decision rule.
Know when precision-recall is clearer
For rare positives, precision-recall curves may expose practical performance better than ROC alone. Interviewers may reward explaining why the metric choice depends on the base rate.
Common mistakes
Candidates often treat AUC as a final verdict. A stronger answer says what AUC captures, what it ignores, and which validation metric would matter next.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.