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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0.3.0 by Florian Pfisterer, 14 days ago

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

Documentation:   PDF Manual  

LGPL (>= 3) license

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

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

See at CRAN