Observability & Evaluation · Data artifact
Holdout Evaluation Set
Data artifactObservability & EvaluationObservability & Evaluationarc:HoldoutEvaluationSet
A sequestered evaluation dataset never consulted during configuration exploration, used only once configuration decisions are final to measure generalisation.
Responsibility. Provides an unbiased generalisation estimate for finalised configurations.
Also known as: Holdout Set, Final Evaluation Set, Rule validation dataset, Validation set
Relationships
is read by dependency
Design guidance
- MUST NOT be used to guide configuration exploration; evaluate on it only after configuration decisions are finalised.
Quantitative guidance
As stated by the sources; verify before use.
- A tuning-vs-holdout gap > 5-7 accuracy points signals overfitting (Ch3.4).
Classification
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
- Risks mitigated
- Information leakage from test set into configuration selection
Sources
- Ch3.4: T. Nguyen, "Tuning Model Parameters for Production Performance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.4. ISBN: 9798244538229.
- Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.
- Ch5.11: T. Nguyen, "Rule-Based Decision Making Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.11. ISBN: 9798244538229.
- Ch7.5: T. Nguyen, "NeMo Curator, Riva Speech AI & Multimodal Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.5. ISBN: 9798244538229.
- Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
- Ref6.01: S. Schürch, "How to Make Your LLM More Accurate with RAG & Fine-Tuning," Towards Data Science, Mar. 11, 2025. [Online]. Available: https://towardsdatascience.com/how-to-make-your-llm-more-accurate-with-rag-fine-tuning/