Explain a demand forecast to an inventory planner.
Show how much the prediction may deviate and under which conditions.
Choose two appropriate practices.
Show error distributions and compare performance across seasons or product conditions instead of presenting a point estimate as certain.
Detailed explanation
Users cannot interpret what the metric means.
Variation and large errors matter beyond the mean.
Error can change with season and operating conditions.
Forecasts include uncertainty and can be wrong.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01ガイドDomain 1.2・1.3の予測ユースケースと評価を確認する。
Official references
What to confirm
平均誤差だけでなく、条件別性能と不確実性を説明できる。
Key points
- Error distribution
- Conditional performance
- Uncertainty
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
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Point estimate
A single prediction omits how errors vary and can appear more certain than the evidence supports.
Contextual error
A model may perform differently across time, populations, or operating conditions.