Related papers: Cyclic Boosting -- an explainable supervised machine learning algorithm

Cyclic Boosting -- an explainable supervised machine learning algorithm

URL: http://arxiv.org/abs/2002.03425v3
Date: Tue, 5 Jan 2021 16:17:14 GMT
Title: Cyclic Boosting -- an explainable supervised machine learning algorithm
Authors: Felix Wick and Ulrich Kerzel and Michael Feindt
Abstract summary: We propose the novel "Cyclic Boosting" machine learning algorithm. It 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.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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