SMAC3 [Lindauer et al., 2022] offers a robust and flexible framework for Bayesian Optimization to support users in determining well-performing hyperparameter configurations for their (Machine Learning) algorithms, datasets and applications at hand. The main core consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better. … Continue reading SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization