Concept dictionary
Evaluation Metric
A metric is a measurement used to evaluate how well a model, workflow, or system is performing.
- Review state
- Reviewed in POC
- Freshness
- Current
- Version
- 2
Also called
- Evaluation Metric
- Metric
Remember it this wayA metric measures a slice of performance, not the whole system.
In plain language
An evaluation metric is a defined measurement used to judge some aspect of system performance, such as accuracy, error, latency, cost, safety, or user success.
Why it matters
Metrics turn a goal into something observable, but every metric emphasizes some outcomes and ignores others. Teams need measures that match the real use case and its harms.
Example
A support classifier is evaluated with recall for urgent cases, false-positive rate, response latency, and escalation outcomes rather than accuracy alone.
Common misconception
A high metric score does not prove that a system is useful, safe, or fair. A metric can be valid for one population or task and misleading for another.
Related terms
Sources
- Google for Developers, “Machine Learning Glossary”provides established boundaries for core machine-learning vocabulary.
- NIST, “The Language of Trustworthy AI”provides a use-case-neutral vocabulary foundation for AI and machine learning.
Learn more
- Compare [[evaluation-gap|Evaluation Gap]], [[verification|Verification]], [[model-card|Model Card]], and [[calibration|Calibration]] to place Evaluation Metric in the surrounding system.
- Follow the cited source material above for the method, standard, or research context behind this draft.