Evaluate a fraud detector.
Explain misses and false alarms.
Choose two correct statements.
A true positive is correctly detected fraud and a false negative is fraud missed as normal; business cost matters beyond accuracy.
Detailed explanation
The target class is correctly detected.
The target class is correctly detected.
It represents a missed fraud case.
It represents a missed fraud case.
It is a normal case incorrectly flagged.
It is a normal case incorrectly flagged.
Class imbalance and error cost can be hidden.
Class imbalance and error cost can be hidden.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 3.3の分類評価、混同行列、適合率・再現率を確認する。Expected result
偽陽性と偽陰性の意味を業務影響へ結び付けられる。Key points
- True positive
- False positive
- False negative
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.