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
A group predicted at 0.8 should have an approximately corresponding observed rate if calibrated.
A group predicted at 0.8 should have an approximately corresponding observed rate if calibrated.
The threshold connects model output to business cost.
The threshold connects model output to business cost.
A probability is not an individual guarantee.
A probability is not an individual guarantee.
Ranking can be useful while probability calibration is poor.
Ranking can be useful while probability calibration is poor.
Try it yourself
An example you can run in a temporary verification environment.
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。Expected result
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
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.