# Supervised learning

> Training a model from features and their corresponding labels. Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, a student can then provide answers to new (never-before-seen) questions on the same topic.[^1]

Training a [model](https://wiki.g15e.com/pages/Model%20(machine%20learning.txt)) from [features](https://wiki.g15e.com/pages/Feature%20(machine%20learning.txt)) and their corresponding [labels](https://wiki.g15e.com/pages/Label%20(machine%20learning.txt)). Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, a student can then provide answers to new (never-before-seen) questions on the same topic.[^1]

## Types

- [Regression model](https://wiki.g15e.com/pages/Regression%20model.txt)
- [Classification model](https://wiki.g15e.com/pages/Classification%20model.txt)

## Evaluation

We evaluate a trained model to determine how well it learned. When we evaluate a model, we use a labeled dataset, but we only give the model the dataset's [features](https://wiki.g15e.com/pages/Feature%20(machine%20learning.txt)). We then compare the model's [predictions](https://wiki.g15e.com/pages/Prediction%20(machine%20learning.txt)) to the label's true values. … Depending on the model's predictions, we might do more [training](https://wiki.g15e.com/pages/Training%20(machine%20learning.txt)) and evaluating before deploying the model in a real-world application.[^2]

## See also

- <Unsupervised learning>

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#supervised-machine-learning
[^2]: https://developers.google.com/machine-learning/intro-to-ml/supervised