A data scientist needs to compare two classifiers when the class distribution is highly imbalanced (95% negative, 5% positive). Which metric is most appropriate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because AUC measures discriminative ability across all classification thresholds and is insensitive to class imbalance, making it reliable when one class dominates. A is wrong because a naive classifier that always predicts the majority class would achieve 95% accuracy despite zero predictive power.
Full explanation below image
Full Explanation
B is correct because AUC measures discriminative ability across all classification thresholds and is insensitive to class imbalance, making it reliable when one class dominates. A is wrong because a naive classifier that always predicts the majority class would achieve 95% accuracy despite zero predictive power. C and D are regression metrics and are not applicable to classification problems.