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Introducing HoNCAML: Holistic No-Code Auto Machine Learning |
XsmfpPUYvq |
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. |
inproceedings |
Proceedings of Machine Learning Research |
PMLR |
2640-3498 |
piras24a |
0 |
Introducing HoNCAML: Holistic No-Code Auto Machine Learning |
17/1 |
27 |
17/1-27 |
17 |
false |
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 |
|
2024-10-09 |
Proceedings of the Third International Conference on Automated Machine Learning |
256 |
inproceedings |
|