Fraud behavior changes and the relationship between features and labels shifts.
Reevaluate the model.
Choose two appropriate responses.
Monitor period-based performance and feature-label relationships, then evaluate retraining on recent examples before an approved switch.
Detailed explanation
A changing relationship can appear even if input proportions are stable.
A changing relationship can appear even if input proportions are stable.
The model can adapt through controlled change management.
The model can adapt through controlled change management.
Concept drift can occur without covariate drift.
Concept drift can occur without covariate drift.
The actual problem becomes harder to diagnose.
The actual problem becomes harder to diagnose.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2・1.3のモデルドリフトとライフサイクルを確認する。Expected result
データ分布の変化と、入力とラベルの関係変化を区別できる。Key points
- Concept drift
- Period metrics
- Retraining
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.