An Interactive Web Application for Decision Tree Learning

Miriam Elia, Carola Gajek, Alexander Schiendorfer, Wolfgang Reif
Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 141:11-16, 2021.

Abstract

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many static resources such as slides or animations out there, interactive visualizations tend to be based on somewhat outdated UI technology and dense in information.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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v141-elia21a, title = {An Interactive Web Application for Decision Tree Learning}, author = {Elia, Miriam and Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, booktitle = {Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {11--16}, year = {2021}, editor = {Bischl, Bernd and Guhr, Oliver and Seibold, Heidi and Steinbach, Peter}, volume = {141}, series = {Proceedings of Machine Learning Research}, month = {14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v141/elia21a/elia21a.pdf}, url = {https://proceedings.mlr.press/v141/elia21a.html}, abstract = {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many static resources such as slides or animations out there, interactive visualizations tend to be based on somewhat outdated UI technology and dense in information.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.} }
Endnote
%0 Conference Paper %T An Interactive Web Application for Decision Tree Learning %A Miriam Elia %A Carola Gajek %A Alexander Schiendorfer %A Wolfgang Reif %B Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2021 %E Bernd Bischl %E Oliver Guhr %E Heidi Seibold %E Peter Steinbach %F pmlr-v141-elia21a %I PMLR %P 11--16 %U https://proceedings.mlr.press/v141/elia21a.html %V 141 %X 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.
APA
Elia, M., Gajek, C., Schiendorfer, A. & Reif, W.. (2021). An Interactive Web Application for Decision Tree Learning. Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 141:11-16 Available from https://proceedings.mlr.press/v141/elia21a.html.

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