SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
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
Text
Lindauer et al. "SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization." Machine Learning Open Source Software, 2022.Markdown
[Lindauer et al. "SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization." Machine Learning Open Source Software, 2022.](https://mlanthology.org/mloss/2022/lindauer2022jmlr-smac3/)BibTeX
@article{lindauer2022jmlr-smac3,
title = {{SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization}},
author = {Lindauer, Marius and Eggensperger, Katharina and Feurer, Matthias and Biedenkapp, André and Deng, Difan and Benjamins, Carolin and Ruhkopf, Tim and Sass, René and Hutter, Frank},
journal = {Machine Learning Open Source Software},
year = {2022},
pages = {1-9},
volume = {23},
url = {https://mlanthology.org/mloss/2022/lindauer2022jmlr-smac3/}
}