Evaluation dataset
Değerlendirme veri kümesi
D4
An evaluation dataset is a defined collection of inputs or reference information used to assess a system for a stated purpose.
Review status: 2026-11-26
Technical explanation
Its documented accuracy, representativeness, relevance, and suitability help define what an evaluation result can describe.
Conceptual boundaries
An evaluation dataset is not automatically representative of deployment conditions; when training, validation, and testing roles are specified, those roles should not be silently conflated.
Provider-neutral example
A service team can keep a labelled set of past, permissioned support requests aside to examine whether a routing model meets its stated routing criteria.
Limitations
Documentation or use as known reference data does not by itself establish that a dataset is representative or that every label is uncontested.
Related concepts
Atomic claims and evidence
1.1NIST AI 600-1, Generative Artificial Intelligence Profile — D4 evidence slice
- Source
- NIST AI 600-1, Generative Artificial Intelligence Profile — D4 evidence slice
- Source role
- Authoritative source
- Exact locator
- MAP 2.3, MP-2.3-002, printed p. 28
- Supported claim
- NIST recommends reviewing and documenting the accuracy, representativeness, relevance, and suitability of data used at different AI lifecycle stages.
- Last verification
- Review due
- Scope limitation
- This guidance does not certify any dataset as representative merely because it is documented.
2.1NIST AI 600-1, Generative Artificial Intelligence Profile — D4 evidence slice
- Source
- NIST AI 600-1, Generative Artificial Intelligence Profile — D4 evidence slice
- Source role
- Authoritative source
- Exact locator
- MAP 2.3 action text, printed p. 27
- Supported claim
- NIST identifies known ground-truth data as one comparison input among varied evaluation methods.
- Last verification
- Review due
- Scope limitation
- Known reference data can support a task-specific comparison; it does not establish that all labels are complete or uncontested.
3.1Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Source
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Source role
- Authoritative source
- Exact locator
- Article 3(29)-(32)
- Supported claim
- The EU AI Act distinguishes training data used to fit learnable parameters, validation data used to evaluate a trained system and tune its non-learnable parameters and learning process, and testing data used to independently evaluate the system before it is placed on the market or put into service.
- Last verification
- Review due
- Scope limitation
- These are the Act's defined roles and do not require every technical workflow to use identical dataset splits.