Fraud is only 0.1% of transactions.
Accuracy alone does not show missed fraud.
Choose two appropriate practices.
Use positive-class metrics such as precision and recall, and state how evaluation sampling relates to the production rate.
Detailed explanation
A majority-class model can have high accuracy while missing all fraud.
A majority-class model can have high accuracy while missing all fraud.
A sampled test set must not be mistaken for production performance.
A sampled test set must not be mistaken for production performance.
It detects no fraud and fails the business goal.
It detects no fraud and fails the business goal.
The important positive-class behavior becomes invisible.
The important positive-class behavior becomes invisible.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドDomain 1.3の分類評価と不均衡データを確認する。Expected result
多数派だけで高く見える正解率の限界を説明できる。Key points
- Imbalance
- Precision
- Recall
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.