supervised learning

Machine learning task of learning a function that maps an input to an output based on example input-output pairs

Also known as supervised machine learning

Instance of: field of study
Subclass of: machine learning method
Part of: machine learning

Overview

From Wikipedia, the free encyclopedia · Read full article · Text CC BY-SA 4.0

In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs. This process involves training a statistical model using labeled data, meaning each piece of input data is provided with the correct output. The term "supervised" refers to the role of a teacher or supervisor who provides this training data, guiding the algorithm towards correct predictions. For instance, if you want a model to identify cats in images, supervised learning would involve feeding it many images of cats (inputs) that are explicitly labeled "cat" (outputs).

Overview

All data

31 statements · 26% with external sources · numbers in brackets are citations

Opposite of: unsupervised learning
ACM Classification Code (2012): 10010259
Used by: data scientist
Studied by: machine learning
Uses: discriminative model

Data quality

statements

26%

externally sourced

Source last edited September 19, 2026. AIDB refreshes this page daily.

Suggest a fact or correction

Links

Use this data

curl https://aidb.si/api/v1/entities/Q334384

API docs, limits & keys

Cite this page

AIDB. (2026). supervised learning (Q334384). Retrieved from https://aidb.si/e/supervised-learning-Q334384