Concept dictionary
Supervised Learning
Supervised training is a method where a model learns from examples that already include the correct answer or label.
- Review state
- Reviewed in POC
- Freshness
- Current
- Version
- 2
Also called
- Supervised Learning
- Supervised Training
Remember it this wayLearn from inputs paired with answers.
In plain language
Supervised learning trains a model from examples paired with the target answer or label. The model learns a mapping it can apply to new inputs.
Why it matters
The method makes the target explicit, which supports direct evaluation. Its quality still depends on label meaning, coverage, consistency, and whether future data resembles the training examples.
Example
A classifier trains on email messages labeled “phishing” or “legitimate,” then predicts a label for new messages.
Common misconception
Supervised learning does not mean a person watches every training step. The supervision is carried by the labeled examples and chosen objective.
Related terms
Sources
- Google for Developers, “Machine Learning Glossary”provides established boundaries for core machine-learning vocabulary.
Learn more
- Compare [[training|Training]], [[ml-target|ML Target]], [[dataset|Dataset]], and [[unsupervised-learning|Unsupervised Learning]] to place Supervised Learning in the surrounding system.
- Follow the cited source material above for the method, standard, or research context behind this draft.