# Regression model

> Informally, a model that generates a numerical prediction. (In contrast, a classification model generates a class prediction.) For example, the following are all regression models:[^1]

Informally, a [model](https://wiki.g15e.com/pages/Model%20(machine%20learning.txt)) that generates a numerical prediction. (In contrast, a [classification model](https://wiki.g15e.com/pages/Classification%20model.txt) generates a [class](https://wiki.g15e.com/pages/Class%20(machine%20learning.txt)) prediction.) For example, the following are all regression models:[^1]

- A model that predicts a certain house's value, such as 423,000 Euros.
- A model that predicts a certain tree's life expectancy, such as 23.2 years.
- A model that predicts the amount of rain that will fall in a certain city over the next six hours, such as 0.18 inches.

Not every model that outputs numerical predictions is a regression model. In some cases, a numeric prediction is really just a classification model that happens to have numeric class names. For example, a model that predicts a numeric postal code is a classification model, not a regression model.[^1]

## Types of regression models

- [Linear regression](https://wiki.g15e.com/pages/Linear%20regression.txt), which finds the line that best fits label values to features.
- [Logistic regression](https://wiki.g15e.com/pages/Logistic%20regression.txt), which generates a probability between 0.0 and 1.0 that a system typically then maps to a class prediction.

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#regression_model