Comparing hyperparameter tuning strategies with tidymodels

We compare 5 hyperparameter tuning search strategies in terms of (a) model quality and (b) run time on 10 learning problems with 3 machine learning algorithms using the tidymodels framework. Bayesian search gave best results, while the racing methods had lowest running time. When interpreting the results, it must be taken into account that the search strategies have their own hyperparameters, which can substantially influence the results.