Latest revision as of 19:08, 3 February 2021

Abstract

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases.

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1908.05557

• http://arxiv.org/pdf/1908.05557

• http://xplorestaging.ieee.org/ielx7/8970220/8995176/08995391.pdf?arnumber=8995391,

http://dx.doi.org/10.1109/ictai.2019.00209

• https://dblp.uni-trier.de/db/conf/ictai/ictai2019.html#TruongWGHBF19,

https://arxiv.org/pdf/1908.05557.pdf,

https://arxiv.org/abs/1908.05557,

http://export.arxiv.org/pdf/1908.05557,

http://export.arxiv.org/abs/1908.05557,

https://academic.microsoft.com/#/detail/3005880794