Hyperparameter Tuning

The Online Machine Learning (OML) methods presented in the previous chapters require the specification of many hyperparameters. For example, a variety of “splitters” are available for Hoeffding trees to generate subtrees. There are different methods for limiting the tree size in order to keep the time and memory requirements within reasonable limits. In addition, there are many other parameters, so that a manual search for the optimal hyperparameter setting is very time-consuming and doomed to fail due to the complexity of the possible combinations. Therefore, this chapter explains how an automatic optimization (or “tuning”) of the hyperparameters can be performed. In addition to the optimization of the OML procedure, Hyperparameter Tuning (HPT) performed with the Sequential Parameter Optimization Toolbox (SPOT) is also important for the explainability and interpretability of OML procedures and can lead to a more efficient and thus resource-saving algorithm (“Green IT”).