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)