MLS-C01 Modeling Practice Question
A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents only 2% of the data. The model achieves 99% accuracy but only identifies 5% of actual positives. Which metric should the scientist use to evaluate 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 (sensitivity) measures the proportion of actual positives correctly identified, which is the key concern here. Accuracy is misleading due to class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy is high due to majority class but does not reflect poor positive detection.
- ✗
F1-score
Why it's wrong here
F1 combines precision and recall but recall alone is more diagnostic here.
- ✗
Precision
Why it's wrong here
Precision measures correctness of positive predictions, not detection rate.
- ✓
Recall
Why this is correct
Recall directly measures the fraction of actual positives captured.
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