An interactive web application for decision tree learning

  • Decision tree learning offers an intuitive and straightforward introduction to machine learning techniques, especially when students are used to program imperative code. Most commonly, trees are trained using a greedy algorithm based on information-theoretic criteria. While there are many static resources such as slides or animations out there, interactive visualizations tend to be based on somewhat outdated UI technology and dense in information. We propose a clean and simple web application for decision tree learning that is extensible and open source.

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Metadaten
Author:Miriam Elia, Carola GajekORCiDGND, Alexander SchiendorferORCiDGND, Wolfgang ReifORCiDGND
URN:urn:nbn:de:bvb:384-opus4-797115
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/79711
URL:https://openreview.net/forum?id=aLdr-6rFn5j
Parent Title (English):Proceedings of the Teaching Machine Learning Workshop at ECML-PKDD 2020, 14 September 2020
Publisher:ML Research Press
Type:Conference Proceeding
Language:English
Year of first Publication:2020
Publishing Institution:Universität Augsburg
Release Date:2020/09/25
Series:PMLR - Proceedings of Machine Learning Research
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Latest Publications (not yet published in print):Latest Publications (not yet published in print)
Licence (German):Deutsches Urheberrecht