Review Supervised Learning

Supervised training is a method where a model learns from examples that already include the correct answer or label.

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.

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.

A classifier trains on email messages labeled “phishing” or “legitimate,” then predicts a label for new messages.

Supervised learning does not mean a person watches every training step. The supervision is carried by the labeled examples and chosen objective.

Learn from inputs paired with answers.

* [[training|Training]] * [[ml-target|ML Target]] * [[dataset|Dataset]] * [[unsupervised-learning|Unsupervised Learning]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary.

* 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.