Scipediacontent: Scipediacontent moved page Draft Content 269631716 to Farivar et al 2019a

2021-02-03T18:08:35Z

Scipediacontent moved page Draft Content 269631716 to Farivar et al 2019a

Scipediacontent: Created page with " == 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 hi..."

2021-02-03T18:08:32Z

Created page with " == 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 hi..."

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== 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/abs/1908.05557]

* [http://arxiv.org/pdf/1908.05557 http://arxiv.org/pdf/1908.05557]

* [http://xplorestaging.ieee.org/ielx7/8970220/8995176/08995391.pdf?arnumber=8995391 http://xplorestaging.ieee.org/ielx7/8970220/8995176/08995391.pdf?arnumber=8995391],
: [http://dx.doi.org/10.1109/ictai.2019.00209 http://dx.doi.org/10.1109/ictai.2019.00209]

* [https://dblp.uni-trier.de/db/conf/ictai/ictai2019.html#TruongWGHBF19 https://dblp.uni-trier.de/db/conf/ictai/ictai2019.html#TruongWGHBF19],
: [https://arxiv.org/pdf/1908.05557.pdf https://arxiv.org/pdf/1908.05557.pdf],
: [https://arxiv.org/abs/1908.05557 https://arxiv.org/abs/1908.05557],
: [http://export.arxiv.org/pdf/1908.05557 http://export.arxiv.org/pdf/1908.05557],
: [http://export.arxiv.org/abs/1908.05557 http://export.arxiv.org/abs/1908.05557],
: [https://academic.microsoft.com/#/detail/3005880794 https://academic.microsoft.com/#/detail/3005880794]

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Scipediacontent: Scipediacontent moved page Draft Content 269631716 to Farivar et al 2019a

Scipediacontent: Created page with " == 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 hi..."