A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically - AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms.
@article{arxiv.1907.08392,
title = {Automated Machine Learning in Practice: State of the Art and Recent Results},
author = {Lukas Tuggener and Mohammadreza Amirian and Katharina Rombach and Stefan Lörwald and Anastasia Varlet and Christian Westermann and Thilo Stadelmann},
journal= {arXiv preprint arXiv:1907.08392},
year = {2019}
}
Comments
Accepted full paper at SDS2019, the 6th Swiss Conference on Data Science