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AIF-C01 Practice Question: During a binary classification project, the team…
During a binary classification project, the team wants to optimize for correctly identifying positive cases even if it means more false positives. Which metric should they maximize?
⚠ Common exam trap
The distinction between recall and precision is critical. By emphasizing catching all positives, recall is the correct metric; precision would be chosen if the scenario prioritized minimizing false 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
Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases that are correctly identified. By maximizing recall, the model minimizes false negatives, which aligns with the goal of catching as many true positives as possible, even at the cost of increasing false positives.
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 measures the proportion of actual positives correctly identified, so maximising it directly satisfies the goal of catching positive cases. Precision would instead penalise the extra false positives the team is willing to accept, making recall the metric aligned with this trade-off.
- ✗
Precision
Why it's wrong here
Precision measures how many predicted positives are truly positive, so maximising it discourages the false positives the team is willing to tolerate. It is tempting because precision is a core classification metric, and would be correct when the cost of a false positive outweighs missing a positive case.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC aggregates performance across all thresholds, so it does not specifically reward catching positives at the expense of false positives. It is tempting because it is the standard summary metric for binary classifiers, and would be the right choice when comparing overall ranking quality across thresholds rather than targeting recall.
- ✗
F1 score
Why it's wrong here
F1 is the harmonic mean of precision and recall, so it penalises the false positives the scenario explicitly accepts, pulling the score down. It is tempting because F1 balances both error types, and would be correct when false positives and false negatives carry comparable cost and neither should dominate.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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