ROC Curves and AUC Quiz

Answers and explanations appear as you take the quiz.

1. What two quantities does an ROC curve plot against each other?
   • True Positive Rate vs. False Positive Rate
   • Precision vs. Recall
   • Accuracy vs. Threshold
   • Sensitivity vs. Precision
2. What AUC value corresponds to a classifier that performs no better than random guessing?
   • 0.0
   • 0.5
   • 0.75
   • 1.0
3. AUC can be interpreted as the probability that the model:
   • Correctly classifies a randomly chosen sample
   • Achieves over 90% accuracy
   • Scores a random positive higher than a random negative
   • Has zero false positives
4. Why is AUC described as 'threshold-independent'?
   • It only works at a 0.5 threshold
   • It ignores the positive class entirely
   • It requires calibrated probabilities
   • It integrates performance across all possible thresholds
5. For a heavily imbalanced dataset, which curve is often recommended alongside or instead of ROC because ROC can look overly optimistic?
   • Precision-Recall curve
   • Learning curve
   • Calibration curve
   • Loss curve
6. A point in the top-left corner of ROC space represents:
   • High false positives and low true positives
   • Perfect or near-perfect separation
   • Random performance
   • A miscalibrated model
7. Why can a model have a high ROC-AUC but poorly calibrated probability estimates?
   • AUC directly measures the distance between every predicted probability and its true label
   • Calibration is guaranteed whenever the ROC curve is above the diagonal
   • AUC depends on score ranking, so a monotonic score transformation can preserve ranking while changing probability values
   • AUC uses only one fixed classification threshold
8. What does partial AUC let an evaluator emphasize?
   • Only the model's training loss
   • Probability calibration across all score values
   • The number of labels in a multiclass model
   • Performance in a selected false-positive-rate region, such as the low-FPR range