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
Here's the deal — you want Area Under the ROC Curve (AUC) because it measures discriminative ability across all classification thresholds and stays reliable when one class dominates. Accuracy can look great when a naive classifier always predicts the majority class, even though it has zero predictive power.
Full Explanation
Area Under the ROC Curve (AUC) is the most appropriate metric because it measures discriminative ability across all classification thresholds and is insensitive to class imbalance, making it reliable when one class dominates. Accuracy is wrong because a naive classifier that always predicts the majority class would achieve 95% accuracy despite zero predictive power. Sum of Squared Errors is wrong because it is a regression metric and is not applicable to classification problems. R-squared is wrong because it is also a regression metric and is not applicable to classification problems.