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MLA-C01 Practice Question: A team is evaluating classification models for a…
A team is evaluating classification models for a medical diagnosis application. The cost of a false negative is much higher than the cost of a false positive. Which metric should be optimized during model selection?
⚠ Common exam trap
Test-takers frequently default to F1 score as a 'balanced' metric, forgetting that when costs are asymmetric, the metric must reflect the specific business or clinical cost structure, not a generic harmonic mean.
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 directly minimizes false negatives. In medical diagnosis, missing a disease (false negative) is far more costly than a false alarm, so optimizing recall ensures the model captures as many true positive cases as possible.
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 minimizes false negatives, directly addressing the high cost of missed diagnoses.
- ✗
Accuracy
Why it's wrong here
Accuracy treats false positives and false negatives equally, which is suboptimal when one class is more important.
- ✗
F1 score
Why it's wrong here
F1 balances precision and recall, but does not give extra weight to recall.
- ✗
Precision
Why it's wrong here
Precision reduces false positives, which is not the primary concern.
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