SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
This provides a robust and flexible tool for users in machine learning and optimization to enhance algorithm performance, though it is incremental as it builds on existing Bayesian optimization frameworks.
The paper tackles the problem of efficiently optimizing hyperparameters and algorithm configurations by introducing SMAC3, a versatile Bayesian optimization package that improves performance within a few evaluations.
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.