Build a churn prediction model.
Use only information available at prediction time and predict a future outcome.
Which data preparation is correct?
Use prediction-time customer attributes as features and future churn as the label, while recording timing and meaning.
Detailed explanation
This prevents future information from entering the inputs.
This prevents future information from entering the inputs.
That value is unavailable at prediction time and causes leakage.
That value is unavailable at prediction time and causes leakage.
The model would receive the answer directly.
The model would receive the answer directly.
Leakage and reproducibility could not be checked.
Leakage and reproducibility could not be checked.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01試験ガイドDomain 1.2の特徴量、ラベル、データリーケージを確認する。Expected result
特徴量とラベルを時間軸・可用性で区別できる。Key points
- Features
- Labels
- Observation time
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.