labelnoise: Find Label Errors in Classification Data with Confident Learning

Finds examples whose given class label is likely wrong, using out-of-sample predicted probabilities from any classifier. Implements confident learning (Northcutt, Jiang and Chuang, 2021) <doi:10.1613/jair.1.12125>: per-class confidence thresholds, the confident joint, calibrated estimates of the joint distribution of given and true labels, noise transition matrices, and five rank-and-prune rules for flagging label issues. Also provides model-agnostic label quality scores (Kuan and Mueller, 2022) <doi:10.5281/zenodo.18168246> and a per-class label health summary. Inputs can be a label vector with a probability matrix, a data frame of predictions, resampling results from 'tune', predictions from 'mlr3', or a fitted 'caret' model.

Version:	0.1.0
Depends:	R (≥ 4.1.0)
Imports:	cli, rlang (≥ 1.1.0), stats, utils, tibble, tidyselect
Suggests:	caret, knitr, lgr, mlr3, nnet, parsnip, rmarkdown, rsample, testthat (≥ 3.0.0), tune, withr, workflows, yardstick
Published:	2026-11-13
DOI:	10.32614/CRAN.package.labelnoise (may not be active yet)
Author:	Ridson Al Farizal P [aut, cre, cph]
Maintainer:	Ridson Al Farizal P <ridsonalfarizal15 at gmail.com>
BugReports:	https://github.com/ridsonap/labelnoise/issues
License:	MIT + file LICENSE
URL:	https://ridsonap.is-a.dev/labelnoise/, https://github.com/ridsonap/labelnoise
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	labelnoise results

Documentation:

Reference manual:	labelnoise.html , labelnoise.pdf
Vignettes:	Finding label errors with labelnoise (source, R code)

Downloads:

Package source:	labelnoise_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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