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SMAC3

A Versatile Bayesian Optimization Package for Hyperparameter Optimization

verfasst von
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.

Organisationseinheit(en)
Fachgebiet Maschinelles Lernen
Externe Organisation(en)
Albert-Ludwigs-Universität Freiburg
Bosch Center for Artificial Intelligence (BCAI)
Typ
Artikel
Journal
Journal of Machine Learning Research
Band
2022
Anzahl der Seiten
8
ISSN
1532-4435
Publikationsdatum
02.2022
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
Elektronische Version(en)
https://arxiv.org/abs/2109.09831 (Zugang: Offen)
https://www.jmlr.org/papers/volume23/21-0888/21-0888.pdf (Zugang: Offen)