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.
Author: | Joachim Engel, Tim Erickson |
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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): | Creative Commons - CC BY - Namensnennung 4.0 International |