Multi-Calibration Boosting

Implements 'Multi-Calibration Boosting' (2018) <> and 'Multi-Accuracy Boosting' (2019) 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.


Reference manual

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0.4.0 by Florian Pfisterer, 11 days ago

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Authors: Florian Pfisterer [cre, aut] , Susanne Dandl [ctb] , Christoph Kern [ctb] , Carolin Becker [ctb] , Bernd Bischl [ctb]

Documentation:   PDF Manual  

LGPL (>= 3) license

Imports backports, checkmate, data.table, mlr3, mlr3misc, mlr3pipelines, mlr3proba, R6, rpart, glmnet

Suggests curl, lgr, formattable, tidyverse, PracTools, mlr3learners, mlr3oml, neuralnet, paradox, testthat, knitr, ranger, rmarkdown, survival, xgboost, covr

See at CRAN