SMAC3

A Versatile Bayesian Optimization Package for Hyperparameter Optimization

authored by: Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter
Abstract: Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at github.com/automl/SMAC3.
Organisation(s): Machine Learning Section
External Organisation(s): University of Freiburg
Bosch Center for Artificial Intelligence (BCAI)
Type: Article
Journal: Journal of Machine Learning Research
Volume: 2022
No. of pages: 8
ISSN: 1532-4435
Publication date: 02.2022
Publication status: Published
Peer reviewed: Yes
Electronic version(s): https://arxiv.org/abs/2109.09831 (Access: Open)
https://www.jmlr.org/papers/volume23/21-0888/21-0888.pdf (Access: Open)