  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.
