Question 43 of 1,672
MLS-C01 Recall Practice Question
A data scientist is training a binary classifier using logistic regression on a dataset that is highly imbalanced (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class. Which metric should the scientist use to evaluate the model's performance on the positive class?
⚠ Common exam trap
Candidates may think precision is better because it focuses on correctness of positive predictions, but when the model never predicts positives, precision is undefined. Recall directly highlights the model's inability to find positives.
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
The model only predicts the negative class, so recall (true positives / (true positives + false negatives)) is 0, directly revealing the failure to detect positives. Precision is undefined (or 0/0) because there are no positive predictions. Accuracy is misleading (95% but useless). Therefore, recall is the most informative metric for evaluating performance on the positive class.
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
Correct. Recall measures the proportion of actual positives correctly identified. Since the model never predicts positive, recall is 0, which accurately reflects the model's inability to detect positive instances.
- ✗
Precision
Why it's wrong here
Incorrect. Precision is the proportion of positive predictions that are correct. When the model makes no positive predictions, precision is undefined (or 0/0) and not informative. It does not reveal the model's failure to recall positives.
- ✗
F1 Score
Why it's wrong here
Incorrect. F1 Score is the harmonic mean of precision and recall. Since precision is undefined, F1 is also undefined. It does not provide a clear indication of the problem.
- ✗
Accuracy
Why it's wrong here
Incorrect. Accuracy is 95% because the model always predicts the majority class, which masks the complete failure to identify positives. It is misleading for imbalanced datasets.
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Last reviewed: Jun 20, 2026
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