Description

Hyperparameter tuning? Is this relevant in practice? Is it not rather an academic

gimmick? That the latter is not the case has been known for many years. On the other

hand, it is mostly unclear what exactly this looks like in practice. Which procedures

depend on which hyperparameters? How sensitive are the procedures to different

settings of their hyperparameters? And does that in turn depend on which data

constellations are available? How can users develop a good feeling for being on

the right track when tuning? Answers to these questions are not only expected when

it comes to optimally performing tuning per se, but also when it comes to making

the tuning process transparent, i.e., answering the question why, after all, this and

not that hyperparameter constellation was chosen.

This book delivers answers to the above questions, some of which were compiled

as part of a study funded by the Federal Statistical Office of Germany. The

contributed case studies and associated scripts also enable practitioners to reproduce

the described tuning procedures and apply them themselves. The presented

insights, cross-references, experiences, and recommendations will contribute to a

better understanding of hyperparameter tuning in machine learning and to gain

transparency.