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Supervised Learning

In Supervised Learning, the machine learns under the supervision. It contains a model that is able to predict with the help of a labeled dataset. A labeled dataset is one where one already knows the target answer.

Introduction

Supervised learning can be further divided into two types:

Classification - Classification is used when the output variable is categorical i.e. with two or more classes. For example, yes or no, male or female, true or false, etc.
Regression - Regression is used when the output variable is a real or continuous value. In this case, there is a relationship between two or more variables i.e., a change in one variable is associated with a change in the other variable. For example, salary based on work experience or weight based on height, etc.

Source: https://www.simplilearn.com/tutorials/machine-learning-tutorial/supervised-and-unsupervised-learning#difference_between_supervised_and_unsupervised_learning

Outgoing relations

Supervised Learning is a kind of Machine Learning
Supervised Learning is part of Agent Based Modelling

Incoming relations

Decision Tree is a kind of Supervised Learning
Multi-layer perceptron is a kind of Supervised Learning
Random Forest is a kind of Supervised Learning

Supervised Learning

Introduction﻿

Outgoing relations

Incoming relations

Introduction