  Machine learning algorithms such as random forests or xgboost are gaining
more importance and are increasingly incorporated into production processes in
order to enable comprehensive digitization and, if possible, automation of
processes. Hyperparameters of these algorithms used have to be set
appropriately, which can be referred to as hyperparameter tuning or
optimization. Based on the concept of tunability, this article presents an
overview of theoretical and practical results for popular machine learning
algorithms. This overview is accompanied by an experimental analysis of 30
hyperparameters from six relevant machine learning algorithms. In particular,
it provides (i) a survey of important hyperparameters, (ii) two parameter
tuning studies, and (iii) one extensive global parameter tuning study, as well
as (iv) a new way, based on consensus ranking, to analyze results from multiple
algorithms. The R package mlr is used as a uniform interface to the machine
learning models. The R package SPOT is used to perform the actual tuning
(optimization). All additional code is provided together with this paper.
