Teaching Machine Learning with mlr3 using Shiny

Gero Szepannel, Laurens Tetzlaff, Alexander Frahm, Karsten Lübke
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:42-45, 2023.

Abstract

The command-line use of R and Python can be a barrier for novice learners in the field of machine learning. Lowering this bar for nontechnical students may help them to understand important core principles of machine learning like training and evaluation of models. Shiny applications can provide an user friendly graphical interface to the machine learning workflow. mlr3shiny uses the functionalities of the R-package mlr3 and provides teachers and learners of machine learning the opportunity to explore the machine learning workflow without the need to learn R programming first.

Cite this Paper

BibTeX
@InProceedings{pmlr-v207-szepannel23a, title = {Teaching Machine Learning with mlr3 using Shiny}, author = {Szepannel, Gero and Tetzlaff, Laurens and Frahm, Alexander and L\"ubke, Karsten}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {42--45}, year = {2023}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {207}, series = {Proceedings of Machine Learning Research}, month = {19--23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v207/szepannel23a/szepannel23a.pdf}, url = {https://proceedings.mlr.press/v207/szepannel23a.html}, abstract = {The command-line use of R and Python can be a barrier for novice learners in the field of machine learning. Lowering this bar for nontechnical students may help them to understand important core principles of machine learning like training and evaluation of models. Shiny applications can provide an user friendly graphical interface to the machine learning workflow. mlr3shiny uses the functionalities of the R-package mlr3 and provides teachers and learners of machine learning the opportunity to explore the machine learning workflow without the need to learn R programming first.} }
Endnote
%0 Conference Paper %T Teaching Machine Learning with mlr3 using Shiny %A Gero Szepannel %A Laurens Tetzlaff %A Alexander Frahm %A Karsten Lübke %B Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2023 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v207-szepannel23a %I PMLR %P 42--45 %U https://proceedings.mlr.press/v207/szepannel23a.html %V 207 %X The command-line use of R and Python can be a barrier for novice learners in the field of machine learning. Lowering this bar for nontechnical students may help them to understand important core principles of machine learning like training and evaluation of models. Shiny applications can provide an user friendly graphical interface to the machine learning workflow. mlr3shiny uses the functionalities of the R-package mlr3 and provides teachers and learners of machine learning the opportunity to explore the machine learning workflow without the need to learn R programming first.
APA
Szepannel, G., Tetzlaff, L., Frahm, A. & Lübke, K.. (2023). Teaching Machine Learning with mlr3 using Shiny. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 207:42-45 Available from https://proceedings.mlr.press/v207/szepannel23a.html.

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