mcboost-package: mcboost: Multi-Calibration Boosting

In mcboost: Multi-Calibration Boosting

mcboost: Multi-Calibration Boosting

Description

Implements 'Multi-Calibration Boosting' (2018) https://proceedings.mlr.press/v80/hebert-johnson18a.html and 'Multi-Accuracy Boosting' (2019) \Sexpr[results=rd]{tools:::Rd_expr_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 [e-mail] [contributor]

Authors:

• Florian Pfisterer [e-mail] (ORCID)

Other contributors:

• Susanne Dandl [e-mail] (ORCID) [contributor]

• Christoph Kern [e-mail] (ORCID) [contributor]

• Carolin Becker [contributor]

• Bernd Bischl [e-mail] (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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.21105/joss.03453")}, https://joss.theoj.org/papers/10.21105/joss.03453.

See Also

Useful links:

• https://github.com/mlr-org/mcboost

• Report bugs at https://github.com/mlr-org/mcboost/issues

mcboost documentation built on Aug. 8, 2025, 6:22 p.m.