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:
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