An open source AutoML benchmark

Pieter Gijsbers; Erin LeDell; Sébastien Poirier; Janek Thomas; Bernd Bischl; Joaquin Vanschoren

An open source AutoML benchmark

Pieter Gijsbers, Erin LeDell, Sébastien Poirier, Janek Thomas, Bernd Bischl, Joaquin Vanschoren

Data Mining

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Samenvatting

In recent years, an active field of research has developed around automated machine learning(AutoML). Unfortunately, comparing different AutoML systems is hard and often doneincorrectly. We introduce an open, ongoing, and extensible benchmark framework whichfollows best practices and avoids common mistakes. The framework is open-source, usespublic datasets and has a website with up-to-date results. We use the framework to conducta thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.

Originele taal-2	Engels
Aantal pagina's	8
Status	Gepubliceerd - jun. 2019
Evenement	6th ICML Workshop on Automated Machine Learning - Long Beach, Verenigde Staten van Amerika Duur: 14 jun. 2019 → 14 jun. 2019 https://sites.google.com/view/automl2019icml/home

Workshop

Workshop	6th ICML Workshop on Automated Machine Learning
Verkorte titel	AutoML@ICML2019
Land/Regio	Verenigde Staten van Amerika
Stad	Long Beach
Periode	14/06/19 → 14/06/19
Internet adres	https://sites.google.com/view/automl2019icml/home

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title = "An open source AutoML benchmark",

abstract = "In recent years, an active field of research has developed around automated machine learning(AutoML). Unfortunately, comparing different AutoML systems is hard and often doneincorrectly. We introduce an open, ongoing, and extensible benchmark framework whichfollows best practices and avoids common mistakes. The framework is open-source, usespublic datasets and has a website with up-to-date results. We use the framework to conducta thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.",

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language = "English",

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AU - LeDell, Erin

AU - Poirier, Sébastien

AU - Thomas, Janek

AU - Bischl, Bernd

AU - Vanschoren, Joaquin

PY - 2019/6

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N2 - In recent years, an active field of research has developed around automated machine learning(AutoML). Unfortunately, comparing different AutoML systems is hard and often doneincorrectly. We introduce an open, ongoing, and extensible benchmark framework whichfollows best practices and avoids common mistakes. The framework is open-source, usespublic datasets and has a website with up-to-date results. We use the framework to conducta thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.

AB - In recent years, an active field of research has developed around automated machine learning(AutoML). Unfortunately, comparing different AutoML systems is hard and often doneincorrectly. We introduce an open, ongoing, and extensible benchmark framework whichfollows best practices and avoids common mistakes. The framework is open-source, usespublic datasets and has a website with up-to-date results. We use the framework to conducta thorough comparison of 4 AutoML systems across 39 datasets and analyze the results.

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M3 - Paper

T2 - 6th ICML Workshop on Automated Machine Learning

Y2 - 14 June 2019 through 14 June 2019

ER -

An open source AutoML benchmark

Samenvatting

Workshop

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