mcboost: Multi-Calibration Boosting

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

Version: 0.4.3
Depends: R (≥ 3.1.0)
Imports: backports, checkmate (≥ 2.0.0), data.table (≥ 1.13.6), mlr3 (≥ 0.10), mlr3misc (≥ 0.8.0), mlr3pipelines (≥ 0.3.0), R6 (≥ 2.4.1), rmarkdown, rpart, glmnet
Suggests: curl, lgr, formattable, tidyverse, PracTools, mlr3learners, mlr3oml, neuralnet, paradox, knitr, ranger, xgboost, covr, testthat (≥ 3.1.0)
Published: 2024-04-12
DOI: 10.32614/CRAN.package.mcboost
Author: Florian Pfisterer ORCID iD [aut], Susanne Dandl ORCID iD [ctb], Christoph Kern ORCID iD [ctb], Carolin Becker [ctb], Bernd Bischl ORCID iD [ctb], Sebastian Fischer [ctb, cre]
Maintainer: Sebastian Fischer <sebf.fischer at>
License: LGPL (≥ 3)
NeedsCompilation: no
Citation: mcboost citation info
Materials: README NEWS
CRAN checks: mcboost results


Reference manual: mcboost.pdf
Vignettes: MCBoost - Basics and Extensions
MCBoost - Health Survey Example


Package source: mcboost_0.4.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mcboost_0.4.3.tgz, r-oldrel (arm64): mcboost_0.4.3.tgz, r-release (x86_64): mcboost_0.4.3.tgz, r-oldrel (x86_64): mcboost_0.4.3.tgz
Old sources: mcboost archive


Please use the canonical form to link to this page.