Send fraud cases above probability 0.8 for human review.
Check whether the probability has a meaningful interpretation.
Choose two appropriate evaluations.
Compare predicted probability bins with observed rates and assess threshold impact, workload, false positives, and misses.
Detailed explanation
The threshold connects model output to business cost.
Ranking can be useful while probability calibration is poor.
A group predicted at 0.8 should have an approximately corresponding observed rate if calibrated.
A probability is not an individual guarantee.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01ガイドDomain 1.3の分類性能、確率、しきい値評価を確認する。
Official references
What to confirm
確率の順位性能と校正、業務しきい値を区別できる。
Key points
- Calibration
- Probability
- Threshold
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
Probability calibration
The agreement between predicted probabilities and observed event frequencies.
Decision threshold
A score or probability boundary used to convert a prediction into an action or class.