Abstract
Original language | English |
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Number of pages | 8 |
Publication status | Published - Jun 2019 |
Event | 6th ICML Workshop on Automated Machine Learning - Long Beach, United States Duration: 14 Jun 2019 → 14 Jun 2019 https://sites.google.com/view/automl2019icml/home |
Workshop
Workshop | 6th ICML Workshop on Automated Machine Learning |
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Abbreviated title | AutoML@ICML2019 |
Country | United States |
City | Long Beach |
Period | 14/06/19 → 14/06/19 |
Internet address |
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Keywords
- AutoML
- benchmark
- open source
Cite this
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An open source AutoML benchmark. / Gijsbers, Pieter; LeDell, Erin; Poirier, Sébastien; Thomas, Janek; Bischl, Bernd; Vanschoren, Joaquin.
2019. Paper presented at 6th ICML Workshop on Automated Machine Learning, Long Beach, United States.Research output: Contribution to conference › Paper › Academic
TY - CONF
T1 - An open source AutoML benchmark
AU - Gijsbers, Pieter
AU - LeDell, Erin
AU - Poirier, Sébastien
AU - Thomas, Janek
AU - Bischl, Bernd
AU - Vanschoren, Joaquin
PY - 2019/6
Y1 - 2019/6
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.
KW - AutoML
KW - benchmark
KW - open source
M3 - Paper
ER -