Customer attributes contain many missing values.
Do not replace every missing value with zero without checking meaning.
Choose two appropriate practices.
Investigate why values are missing, compare imputation, exclusion, or indicators, and apply a consistent pipeline in training and inference.
Detailed explanation
Missing, not collected, and not applicable can require different treatments.
Missing, not collected, and not applicable can require different treatments.
The policy and its bias can be evaluated.
The policy and its bias can be evaluated.
Zero may distort the distribution.
Zero may distort the distribution.
Training-serving skew can result.
Training-serving skew can result.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01ガイドDomain 1.2のデータ品質、欠損値、前処理を確認する。Expected result
欠損値を意味に応じて処理し、訓練・推論の一貫性を保つ理由を説明できる。Key points
- Missing cause
- Imputation
- Consistency
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.