Review Model

A model is the trained mathematical system that takes input and produces output, such as a classifier, generator, or language model.

A model is the learned mathematical component produced by training. Given an input, it calculates an output such as a score, classification, prediction, or generated sequence.

Using model precisely keeps it separate from the application, interface, data pipeline, and policies around it. Most real-world behavior comes from the whole system, not the model alone.

A fraud-detection model receives transaction features and returns a risk score. A separate application decides whether to approve, hold, or escalate the transaction.

A model is not the complete product and does not contain a literal copy of every training example. It is one learned component inside a larger system.

Training produces the model; the system puts it to work.

* [[machine-learning|Machine Learning]] * [[training|Training]] * [[parameters|Parameters]] * [[system|System]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary. * [NIST, “The Language of Trustworthy AI”](https://doi.org/10.6028/NIST.AI.100-3) — provides a use-case-neutral vocabulary foundation for AI and machine learning.

* Compare [[machine-learning|Machine Learning]], [[training|Training]], [[parameters|Parameters]], and [[system|System]] to place Model in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.