Question 1,283 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a dataset where the positive class represents only 1% of the data. The model's accuracy is 99%, but the recall for the positive class is 0%. Which metric should the scientist use to evaluate the model's performance effectively?
⚠ Common exam trap
Test-takers frequently choose ROC AUC (Option A) because it is a common default metric, but they fail to recognize that in severe class imbalance, ROC AUC can be artificially inflated by the dominance of true negatives, whereas PR AUC is the correct choice for evaluating minority class performance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Area under the Precision-Recall curve (PR AUC)
In a highly imbalanced dataset where the positive class is only 1%, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the negative class for all samples, resulting in 0% recall for the positive class. The Area under the Precision-Recall curve (PR AUC) is the correct metric because it focuses on the performance of the positive class by evaluating the trade-off between precision and recall, making it sensitive to changes in the minority class. Unlike ROC AUC, which can be overly optimistic in imbalanced settings due to the large number of true negatives, PR AUC provides a more realistic assessment of model performance for rare events.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Area under the ROC curve (ROC AUC)
Why it's wrong here
ROC AUC can be overly optimistic for imbalanced data.
- ✓
Area under the Precision-Recall curve (PR AUC)
Why this is correct
PR AUC is robust to class imbalance.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✗
F1 score
Why it's wrong here
F1 would be 0 because recall is 0.
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Last reviewed: Jun 24, 2026
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