Reduce missed findings in medical-image screening.
For fraud detection, also understand how many flagged cases are truly fraud.
Choose two correct statements.
Recall measures captured actual positives, while precision measures how many positive predictions are truly positive; both and the threshold may matter.
Detailed explanation
That describes a different metric such as specificity.
It describes false-positive burden among flagged cases.
Precision and recall can trade off.
It is important when missed positives are costly.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01試験ガイドDomain 3.3の分類評価指標を確認する。
Official references
What to confirm
再現率と適合率の分母・業務上の意味を説明できる。
Key points
- Recall
- Precision
- Trade-off
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Recall
The fraction of actual positive cases that a classifier identifies as positive.
Precision
The fraction of predicted positive cases that are actually positive.