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What goes before the CART? Introducing classification trees with Arbor and CODAP

  • This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build-it-yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.

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Metadaten
Author:Joachim Engel, Tim Erickson
DOI:https://doi.org/10.1111/test.12347
Publisher:Wiley Online Library
Document Type:Working Paper
Language:English
Publishing Institution:Pädagogische Hochschule Ludwigsburg
Release Date:2023/11/20
Year of Completion:2023
Tag:classification trees, Arbor
GND Keyword:Statistik; Unterricht
Issue:TEACHING STATISTICS 45, S1
Note:
Volltext ist unter angegebenem DOI abrufbar.
Faculties:Fakultät für Kultur- und Naturwissenschaften
Open Access:Ja
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International