Abstract
The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself. In contrast to current, often black-box systems, GAMA allows users to plug in different AutoML and post-processing techniques, logs and visualizes the search process, and supports easy benchmarking. It currently features three AutoML search algorithms, two model post-processing steps, and is designed to allow for more components to be added.
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Notes
- 1.
A video demonstration can be found at https://youtu.be/angsGMvEd1w.
- 2.
Code and documentation can be found at https://github.com/PGijsbers/gama/.
- 3.
An always up-to-date version of this listing can be found at https://pgijsbers.github.io/gama/master/citing.html.
- 4.
- 5.
Although we could not run these experiments on the same (AWS) hardware, we took care to use the same computational constraints.
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Acknowledgements
This software was developed with support from the Data Driven Discovery of Models (D3M) program run by DARPA and the Air Force Research Laboratory.
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Gijsbers, P., Vanschoren, J. (2021). GAMA: A General Automated Machine Learning Assistant. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_39
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