Decision Tree Learning

Leonardo Vanneschi; Sara Silva

doi:10.1007/978-3-031-17922-8_6

Decision Tree Learning

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Given a training set, Decision Trees (DTs) [Quinlan, 1986] are predictive models represented as trees where each vertex represents a feature, or attribute, and each edge represents a possible value of that attribute. Leaves contain target values and a path from the root to a leaf allows us to make a prediction. Although DTs can be used for a wide variety of tasks [Rokach and Maimon, 2014], we will focus only on classification and regression.

Original language	English
Title of host publication	Lectures on Intelligent Systems
Place of Publication	Cham, Switzerland
Publisher	Springer, Cham
Pages	149-159
Number of pages	11
ISBN (Electronic)	978-3-031-17922-8
ISBN (Print)	978-3-031-17921-1, 978-3-031-17924-2
DOIs	https://doi.org/10.1007/978-3-031-17922-8_6
Publication status	Published - 13 Jan 2023

Publication series

Name	Natural Computing Series
ISSN (Print)	1619-7127

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10.1007/978-3-031-17922-8_6

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Decision Tree Learning

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