Question 971 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 only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect 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) is the correct focus because recall measures the proportion of actual positive cases correctly identified. With only 10% recall, the model is missing most positive cases despite high accuracy due to class imbalance. Improving recall directly addresses the goal of detecting the positive class. Option B (Accuracy) is misleading in imbalanced datasets as it can be high even if the model predicts all negatives. Option C (Precision) measures the proportion of positive predictions that are correct, which may not increase recall. Option D (AUC-ROC) is a global metric that may not reflect improvements in recall specifically.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets.
- ✗
Precision
Why it's wrong here
Precision measures false positives, not missed positives.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC is a global metric that may not directly address low recall.
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Last reviewed: Jun 20, 2026
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