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 is the fraction of true positives among predicted positives, addressing false positives.
✗
F1 score
Why it's wrong here
F1 balances precision and recall, but if false positives are the primary concern, precision alone is more direct.
✗
Recall
Why it's wrong here
Recall minimizes false negatives, not false positives.
✗
Accuracy
Why it's wrong here
Accuracy can be misleading with imbalanced classes and does not specifically address false positives.

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AIF-C01 Fundamentals of AI and ML Practice Question

Why each option matters

Option-by-option breakdown

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