---
title: Classification
description: 'Assigning one or more discrete categories to an input, often by converting model scores or probabilities into labels through a decision rule.'
canonical_url: 'https://darkfactory.dev/glossary/classification'
markdown_url: 'https://darkfactory.dev/glossary/classification.md'
collection: glossary
date_published: '2026-08-03T00:00:00-04:00'
date_modified: '2026-08-26T00:00:00-04:00'
---

# Classification


## Definition

Classification assigns one or more discrete categories to an input. A classifier often produces a score or estimated probability for each class, then a decision rule converts those values into labels.

The class structure is part of the problem definition. In single-label classification, one class is chosen from mutually exclusive options. Multi-label classification allows several labels at once. Hierarchical classification organizes labels into levels, such as animal, bird, and hawk. Binary classification is the two-class case, even when the implementation exposes only the score for the positive class.

## What determines a useful classifier

Accuracy alone can hide the error that matters. The class prevalence, decision threshold, false-positive cost, false-negative cost, calibration, and abstention policy determine how a score becomes an operational decision. A classifier can rank examples well and still perform poorly after a threshold is chosen for the wrong cost tradeoff.

Classes are also human choices. Ambiguous label instructions, changing policies, annotator disagreement, and missing categories can cap performance before model selection begins.

## Distinguish it from nearby terms

- **Regression** predicts a continuous quantity rather than a category.
- **Clustering** groups examples without starting from predefined class labels. A person still has to interpret what the groups mean.
- **Ranking** orders candidates. A later rule may classify the highest-ranked items, but ordering and labeling are different tasks.
- **Detection** finds and localizes instances, such as objects in an image. It often includes classification as one step.

## Check your understanding

A fraud model returns a score of 0.62. The classification is not fully specified until the team defines the threshold, what happens near it, and the cost of blocking a legitimate transaction versus allowing fraud.

## Related terms

- [Regression](https://darkfactory.dev/glossary/regression)
- [Clustering](https://darkfactory.dev/glossary/clustering)

## 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/)