| mcboost-package {mcboost} | R Documentation |
mcboost: Multi-Calibration Boosting
Description
Implements 'Multi-Calibration Boosting' (2018) https://proceedings.mlr.press/v80/hebert-johnson18a.html and 'Multi-Accuracy Boosting' (2019) doi:10.48550/arXiv.1805.12317 for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.
Author(s)
Maintainer: Sebastian Fischer sebf.fischer@gmail.com [contributor]
Authors:
Florian Pfisterer pfistererf@googlemail.com (ORCID)
Other contributors:
Susanne Dandl susanne.dandl@stat.uni-muenchen.de (ORCID) [contributor]
Christoph Kern c.kern@uni-mannheim.de (ORCID) [contributor]
Carolin Becker [contributor]
Bernd Bischl bernd_bischl@gmx.net (ORCID) [contributor]
References
Kim et al., 2019: Multiaccuracy: Black-Box Post-Processing for Fairness in Classification. Hebert-Johnson et al., 2018: Multicalibration: Calibration for the (Computationally-Identifiable) Masses. Pfisterer F, Kern C, Dandl S, Sun M, Kim M, Bischl B (2021). “mcboost: Multi-Calibration Boosting for R.” Journal of Open Source Software, 6(64), 3453. doi:10.21105/joss.03453, https://joss.theoj.org/papers/10.21105/joss.03453.
See Also
Useful links: