---
title: Label
description: 'A target or reference value attached to an example for supervised learning, evaluation, or later analysis.'
canonical_url: 'https://darkfactory.dev/glossary/label'
markdown_url: 'https://darkfactory.dev/glossary/label.md'
collection: glossary
date_published: '2026-08-03T00:00:00-04:00'
date_modified: '2026-08-26T00:00:00-04:00'
---

# Label


## Definition

A label is a target or reference value attached to an example for supervised learning, evaluation, or later analysis. It can be a class, number, span of text, ranking, bounding box, segmentation mask, preference, or structured answer.

A person may assign the label, or it may come from a measurement, policy rule, operational event, another model, or a combination of sources. "Known target" should not be read as unquestionable truth. Medical diagnoses change, fraud is discovered late, policies differ among reviewers, and model-generated labels can reproduce the model's own errors.

## Label quality

The labeling protocol defines the task as much as the data does. Reviewers need instructions, examples, an allowed "uncertain" state, rules for disagreement, and evidence about inter-annotator consistency where judgment is involved. Majority vote can hide a genuinely ambiguous case.

Weak supervision uses noisy or indirect rules and signals to create labels at scale. Pseudo-labeling uses model predictions as temporary targets. Both can be useful when their uncertainty and provenance remain visible.

## Distinguish it from nearby terms

- A **feature** is an input supplied to the model. A label is the target or reference answer.
- A **prediction** is the model's output. It may later become a pseudo-label, but the roles should not be confused during evaluation.
- An **annotation** is a recorded human or machine judgment. It becomes a label when the learning or evaluation task uses it as the target.
- **Ground truth** is a stronger claim that the reference accurately represents reality. Many labels are useful without meeting that standard.

## Operational significance

Version labels with their source, time, policy, and adjudication history. Monitor class balance, disagreement, delayed outcomes, and changes in the process that produces the target. A model trained on consistent old policy may fail a current policy even when its code has not changed.

## Check your understanding

Two reviewers disagree about whether a support ticket is abusive. The dataset should preserve or adjudicate that disagreement under a stated rule instead of treating one reviewer's answer as self-evident truth.

## Related terms

- [Feature](https://darkfactory.dev/glossary/feature)
- [Supervised learning](https://darkfactory.dev/glossary/supervised-learning)

## Evidence and further reading

- [NIST AI Resource Center Glossary](https://airc.nist.gov/glossary/)
- [Google Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)