Implements 'Multi-Calibration Boosting' (2018) < https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
'Multi-Accuracy Boosting' (2019) <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.1805.12317>