A fraud detector must catch most fraud.
False alarms also create investigation cost.
Choose two practices.
Set recall and precision targets from the cost of missed fraud and false alarms, then evaluate a threshold on representative data and monitor changes.
Detailed explanation
Recall measures how many actual positive cases are found.
Recall measures how many actual positive cases are found.
Too many alarms can consume investigation capacity.
Too many alarms can consume investigation capacity.
A majority class can hide missed fraud.
A majority class can hide missed fraud.
The operating point needs representative data.
The operating point needs representative data.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の評価指標、分類、ビジネス要件を確認する。Expected result
正解率だけでなく業務コストを含む指標選択を説明できる。Key points
- Recall
- Precision
- 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.