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Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> 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 [aut], Susanne Dandl [ctb], Christoph Kern [ctb], Carolin Becker [ctb], Bernd Bischl [ctb], Sebastian Fischer [ctb, cre] |
Maintainer: | Sebastian Fischer <sebf.fischer at gmail.com> |
BugReports: | https://github.com/mlr-org/mcboost/issues |
License: | LGPL (≥ 3) |
URL: | https://github.com/mlr-org/mcboost |
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: mcboost_0.4.3.zip, r-release: mcboost_0.4.3.zip, r-oldrel: mcboost_0.4.3.zip |
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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.