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Evaluate Classifier with Precision, Recall, and Fairness Metrics

MetaData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

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This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Evaluate Classifier with Precision, Recall, and Fairness Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Data science team must offline-evaluate a classifier that labels videos as harmful.

Question

What offline evaluation framework would you use? Detail suitable metrics (e.g., precision, recall, PR-AUC), handling class imbalance, ground-truth collection, threshold selection based on business costs, and potential fairness checks.

Hints

Discuss label skew, cost of false positives vs negatives, and calibration.