> For the complete documentation index, see [llms.txt](https://nag-9-s.gitbook.io/machine-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nag-9-s.gitbook.io/machine-learning/terminology.md).

# Terminology

From <https://www.safaribooksonline.com/library/view/apache-spark-2x/9781787126497/74757d2e-dd20-4d7a-8531-2d31716f091e.xhtml>

Machine learning also has some terminologies that are specific to its domain and it is worth discussing them:

* **Feature**

  : For any observation feature represents a set of traits that describes the entity quantitatively. For example, for a car the feature can be its color, car model, number of seats, and so on.
* **Label**

  : Label is a dependent entity whose outcome is related to the values of a feature. Such as in case of features of a car depending on the car model and number of seats and the label could be the maker of the car.

Input variables (X): Features, predictors, explanatory variables, independent variables

Output variables (Y): Response variable, dependent variable