# Cyclic Boosting -- an explainable supervised machine learning algorithm

arXiv: https://arxiv.org/abs/2002.03425 · HTML: https://mlsanity.com/paper/2002.03425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Sub-Setting Algorithm for Training Data Selection in Pattern Recognition](https://mlsanity.com/paper/2110.06527/) (2021)
- [Explainable Boosting Machines with Sparsity -- Maintaining Explainability in High-Dimensional Settings](https://mlsanity.com/paper/2311.07452/) (2023)
- [Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance](https://mlsanity.com/paper/2503.21321/) (2025)

## Abstract (arXiv)

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.