Cyclic Boosting -- an explainable supervised machine learning algorithm
This addresses the issue of interpretability in machine learning for users needing transparent models, though it appears incremental as it builds on existing boosting methods.
The authors tackled the problem of black box models in supervised machine learning by proposing the novel 'Cyclic Boosting' algorithm, which enables accurate regression and classification while providing detailed explainability for individual predictions.
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.