Autor:	Halvari, Tuomas, Nurminen, Jukka K., Mikkonen, Tommi
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Zdroj:	EPTCS 319, 2020, pp. 103-116
Druh dokumentu:	Working Paper
DOI:	10.4204/EPTCS.319.8
Popis:	Automated machine learning (AutoML) systems aim at finding the best machine learning (ML) pipeline that automatically matches the task and data at hand. We investigate the robustness of machine learning pipelines generated with three AutoML systems, TPOT, H2O, and AutoKeras. In particular, we study the influence of dirty data on accuracy, and consider how using dirty training data may help create more robust solutions. Furthermore, we also analyze how the structure of the generated pipelines differs in different cases. Comment: In Proceedings AREA 2020, arXiv:2007.11260
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.02649 Zobrazit plný text záznamu View this record from Arxiv