Multi-Calibration Boosting

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


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install.packages("mcboost")

0.3.3.0 by Florian Pfisterer, 2 months ago


https://github.com/mlr-org/mcboost


Report a bug at https://github.com/mlr-org/mcboost/issues


Browse source code at https://github.com/cran/mcboost


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


Documentation:   PDF Manual  


LGPL (>= 3) license


Imports backports, checkmate, 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