Implements 'Multi-Calibration Boosting' (2018) https://proceedings.mlr.press/v80/hebert-johnson18a.html and 'Multi-Accuracy Boosting' (2019) 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.
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.
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