AIF-C01 Fundamentals of AI and ML Practice Question
Which metric is most appropriate for evaluating a classification model when false positives are costly?
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between precision and recall by framing a cost scenario, and the trap here is that candidates confuse 'costly false positives' with 'costly false negatives' and incorrectly choose recall or F1 score without analyzing which error type is being penalized.
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
✓
Precision
Precision is the most appropriate metric when false positives are costly because it measures the proportion of true positive predictions among all positive predictions (TP / (TP + FP)). A high precision indicates that when the model predicts a positive class, it is very likely correct, minimizing the number of false positives. This directly aligns with the business requirement to avoid costly false alarms.
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 this is correct
Precision measures the proportion of positive predictions that are actually correct, so it directly penalises false positives. When false positives carry high cost, maximising precision minimises those costly incorrect positive classifications, unlike recall or accuracy.
- ✗
F1 score
Why it's wrong here
F1 score is the harmonic mean of precision and recall, weighting both equally; it does not target false positives specifically. It suits balanced datasets where both error types matter. When false positives are costly, precision is the relevant metric.
- ✗
Recall
Why it's wrong here
Recall measures the proportion of actual positives captured, so it optimises for avoiding false negatives and says nothing about the false positives that matter here. It is tempting because recall is the standard pairing with precision, and would be correct when missing a positive case carries the greater cost.
- ✗
Accuracy
Why it's wrong here
Accuracy counts all correct predictions, so a model can score highly while still generating many costly false positives on skewed data. It tempts because it is the default metric, and would be correct only when classes are balanced and both error types cost the same.
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