Terms related to machine learning · 3.3.12
supervised machine learning
machine learning that trains only on labelled data
What this term settles
A governance lead borrows this meaning whenever supervised machine learning appears in a risk assessment or an assurance argument. The borrowing is safe only if the artefact behind it matches the description.
Who it binds
A definition binds nobody on its own. It binds the reader who adopts supervised machine learning in a policy, a contract, a data schema or an assurance argument.
What to do in your practice
Take the firm own definition of supervised machine learning, put it beside this one and mark the differences. Each difference is either a deliberate local choice or an inconsistency to fix.
Evidence an auditor or regulator asks for
- A controlled vocabulary entry for supervised machine learning with the citation 3.3.12, an owner and a review date.An auditor can check that the term is controlled rather than assumed.
- A model record that names the algorithm, the training run and the parameters that changed.Shows supervised machine learning in use in the artefact the firm relies on, not only in a glossary.
- A training log with the data version, the criteria and the result.The model record names the vendor product but not the algorithm or the training run, so the term cannot be traced to an artefact.
Common gaps this page catches
- supervised machine learning is used in the model and training record without a written definition, so two teams can mean different things.
- The entry exists in a glossary but not in the system record, so the two disagree at the point of use.
What to prepare before the amendment lands
Brief the owners who use supervised machine learning most often, then ask each to return one artefact that shows the entry in use. File the artefacts against the register row.
How to test it here
Take one live artefact rather than a template, find the sentence or field where supervised machine learning is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention.
What this page does not claim
The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states supervised machine learning at the claim level "definition" and stops where the public record stops.
Common questions
- What does supervised machine learning mean in ISO 22989?
- machine learning that trains only on labelled data
- What evidence should we keep for 3.3.12?
- A controlled vocabulary entry for supervised machine learning with the citation 3.3.12, an owner and a review date. A model record that names the algorithm, the training run and the parameters that changed.
- Is supervised machine learning a requirement?
- No: supervised machine learning is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it.
Related units
Source. ISO/IEC 22989:2022, Clause 3.3.12, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026.
https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en
ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard.
Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text.
Last updated: 2026-10-21 · Markdown version · Check your vocabulary