  Supervised machine learning algorithms have seen spectacular advances and
surpassed human level performance in a wide range of specific applications.
However, using complex ensemble or deep learning algorithms typically results
in black box models, where the path leading to individual predictions cannot be
followed in detail. In order to address this issue, we propose the novel
"Cyclic Boosting" machine learning algorithm, which allows to efficiently
perform accurate regression and classification tasks while at the same time
allowing a detailed understanding of how each individual prediction was made.
