Question 1,165 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. The model achieves 99% accuracy but only identifies 10% of the actual positive cases. Which metric should the data scientist focus on to evaluate the model's performance on the positive class?
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
✓
Recall
Recall measures the proportion of actual positive cases that are correctly identified. In this imbalanced dataset, the model has high accuracy but low recall (only 10% of positives caught), so recall is the key metric to improve. Option A (Precision) is not the primary focus because it measures how many predicted positives are correct, not coverage. Option C (AUC-ROC) evaluates the model's ability to distinguish classes overall, not specifically the recall of the positive class. Option D (F1 score) is the harmonic mean of precision and recall, but since recall is very low, F1 is also low; however, recall directly addresses the problem of missing positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision measures the accuracy of positive predictions, not the coverage of actual positives.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, which is the key issue.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC measures overall classification performance across thresholds, not specifically recall of the positive class.
- ✗
F1 score
Why it's wrong here
F1 score combines precision and recall, but the primary issue is low recall, so recall alone is more direct.
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Last reviewed: Jun 20, 2026
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