SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Machine Learning
2022-02-09 v2 Machine Learning
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 https://github.com/automl/SMAC3.
Cite
@article{arxiv.2109.09831,
title = {SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization},
author = {Marius Lindauer and Katharina Eggensperger and Matthias Feurer and André Biedenkapp and Difan Deng and Carolin Benjamins and Tim Ruhopf and René Sass and Frank Hutter},
journal= {arXiv preprint arXiv:2109.09831},
year = {2022}
}