1.2Standard
Fraud is only 0.1 percent of transactions.
Measure misses and false alarms.
Choose two evaluations.
× Incorrect
For imbalanced data, use precision, recall, F1, PR curves, and a confusion matrix together with business costs.
Detailed explanation
CorrectCheck precision, recall, F1, and the business cost of misses and false alarms
Minority-class performance is visible.
Minority-class performance is visible.
CorrectUse a confusion matrix and PR curve at the operating threshold
The decision is concrete.
The decision is concrete.
IncorrectTreat high accuracy as proof that all fraud is found
The majority class inflates it.
The majority class inflates it.
IncorrectRemove the minority class from testing
Important errors are hidden.
Important errors are hidden.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2の不均衡データ、混同行列、適合率、再現率を確認する。Expected result
少数クラスの検出性能を業務コストに沿って評価できる。Key points
- Imbalance
- Recall
- Precision
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.
Question IDAWS-AIF-432
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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