Decision Tree Learning

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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 languageEnglish
Title of host publicationLectures on Intelligent Systems
Place of PublicationCham, Switzerland
PublisherSpringer, Cham
Number of pages11
ISBN (Electronic)978-3-031-17922-8
ISBN (Print)978-3-031-17921-1, 978-3-031-17924-2
Publication statusPublished - 13 Jan 2023

Publication series

NameNatural Computing Series
ISSN (Print)1619-7127


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