Explain ROC-AUC vs PR-AUC tradeoffs

Q: Explain ROC-AUC vs PR-AUC tradeoffs

This question evaluates understanding of binary classification evaluation metrics—specifically interpretation and trade-offs between ROC-AUC and PR-AUC—testing conceptual skills in model ranking, sensitivity to class imbalance, and selection of appropriate performance measures within the Machine Learning domain.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

In a binary classification setting, you trained a model that outputs predicted probabilities.

Explain what ROC-AUC and PR-AUC measure, including the axes/curves and the intuition behind each.
When can ROC-AUC look “good” while PR-AUC looks “poor” (or vice versa)? Discuss the role of class imbalance and base rate.
If your business goal is to catch as many positives as possible while reviewing a limited number of cases (e.g., fraud investigation), which metric/curve would you emphasize and why?
Name at least two practical pitfalls/edge cases when using these metrics (e.g., calibration vs ranking, operating-threshold selection, changing prevalence between train and production).

Explain ROC-AUC vs PR-AUC tradeoffs

Quick Overview

Solution

Comments (0)