Introducing HoNCAML: Holistic No-Code Auto Machine Learning

Luca Piras, Joan Albert Erráez Castelltort, Jordi Casals Grifell, Xavier de Juan Pulido, Cirus Iniesta, Marina Rosell Murillo, Cristina Soler Arenys
Proceedings of the Third International Conference on Automated Machine Learning, PMLR 256:17/1-27, 2024.

Abstract

In recent years, Machine Learning (ML) has been changing the landscape of many industries, demanding companies to incorporate ML solutions to stay competitive. In response to this imperative, and with the aim of making this technology more accessible, the emergence of “no-code” AutoML assumes critical significance. This paper introduces HoNCAML (Holistic No-Code Auto Machine Learning), a new AutoML library designed to provide an extensive and user-friendly resource accommodating individuals with varying degrees of coding proficiency and diverse levels of ML expertise, inclusive of non-programmers. The no-code principles are implemented through a versatile interface offering distinct modalities tailored to the proficiency of the end user. The efficacy of HoNCAML is comprehensively assessed through qualitative comparisons with analogous libraries, as well as quantitative performance benchmarks on several public datasets. The results from our experiments affirm that HoNCAML not only stands as an accessible and versatile tool, but also ensures competitive performance across a spectrum of ML tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v256-piras24a, title = {Introducing HoNCAML: Holistic No-Code Auto Machine Learning}, author = {Piras, Luca and Castelltort, Joan Albert Err\'aez and Grifell, Jordi Casals and Pulido, Xavier de Juan and Iniesta, Cirus and Murillo, Marina Rosell and Arenys, Cristina Soler}, booktitle = {Proceedings of the Third International Conference on Automated Machine Learning}, pages = {17/1--27}, year = {2024}, editor = {Eggensperger, Katharina and Garnett, Roman and Vanschoren, Joaquin and Lindauer, Marius and Gardner, Jacob R.}, volume = {256}, series = {Proceedings of Machine Learning Research}, month = {09--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v256/main/assets/piras24a/piras24a.pdf}, url = {https://proceedings.mlr.press/v256/piras24a.html}, abstract = {In recent years, Machine Learning (ML) has been changing the landscape of many industries, demanding companies to incorporate ML solutions to stay competitive. In response to this imperative, and with the aim of making this technology more accessible, the emergence of “no-code” AutoML assumes critical significance. This paper introduces HoNCAML (Holistic No-Code Auto Machine Learning), a new AutoML library designed to provide an extensive and user-friendly resource accommodating individuals with varying degrees of coding proficiency and diverse levels of ML expertise, inclusive of non-programmers. The no-code principles are implemented through a versatile interface offering distinct modalities tailored to the proficiency of the end user. The efficacy of HoNCAML is comprehensively assessed through qualitative comparisons with analogous libraries, as well as quantitative performance benchmarks on several public datasets. The results from our experiments affirm that HoNCAML not only stands as an accessible and versatile tool, but also ensures competitive performance across a spectrum of ML tasks.} }
Endnote
%0 Conference Paper %T Introducing HoNCAML: Holistic No-Code Auto Machine Learning %A Luca Piras %A Joan Albert Erráez Castelltort %A Jordi Casals Grifell %A Xavier de Juan Pulido %A Cirus Iniesta %A Marina Rosell Murillo %A Cristina Soler Arenys %B Proceedings of the Third International Conference on Automated Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Katharina Eggensperger %E Roman Garnett %E Joaquin Vanschoren %E Marius Lindauer %E Jacob R. Gardner %F pmlr-v256-piras24a %I PMLR %P 17/1--27 %U https://proceedings.mlr.press/v256/piras24a.html %V 256 %X In recent years, Machine Learning (ML) has been changing the landscape of many industries, demanding companies to incorporate ML solutions to stay competitive. In response to this imperative, and with the aim of making this technology more accessible, the emergence of “no-code” AutoML assumes critical significance. This paper introduces HoNCAML (Holistic No-Code Auto Machine Learning), a new AutoML library designed to provide an extensive and user-friendly resource accommodating individuals with varying degrees of coding proficiency and diverse levels of ML expertise, inclusive of non-programmers. The no-code principles are implemented through a versatile interface offering distinct modalities tailored to the proficiency of the end user. The efficacy of HoNCAML is comprehensively assessed through qualitative comparisons with analogous libraries, as well as quantitative performance benchmarks on several public datasets. The results from our experiments affirm that HoNCAML not only stands as an accessible and versatile tool, but also ensures competitive performance across a spectrum of ML tasks.
APA
Piras, L., Castelltort, J.A.E., Grifell, J.C., Pulido, X.d.J., Iniesta, C., Murillo, M.R. & Arenys, C.S.. (2024). Introducing HoNCAML: Holistic No-Code Auto Machine Learning. Proceedings of the Third International Conference on Automated Machine Learning, in Proceedings of Machine Learning Research 256:17/1-27 Available from https://proceedings.mlr.press/v256/piras24a.html.

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