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  "Title": "Find Label Errors in Classification Data with Confident Learning",
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  "Description": "Finds examples whose given class label is likely wrong,\nusing out-of-sample predicted probabilities from any\nclassifier. Implements confident learning (Northcutt, Jiang and\nChuang, 2021) <doi:10.1613/jair.1.12125>: per-class confidence\nthresholds, the confident joint, calibrated estimates of the\njoint distribution of given and true labels, noise transition\nmatrices, and five rank-and-prune rules for flagging label\nissues. Also provides model-agnostic label quality scores (Kuan\nand Mueller, 2022) <doi:10.5281/zenodo.18168246> and a\nper-class label health summary. Inputs can be a label vector\nwith a probability matrix, a data frame of predictions,\nresampling results from 'tune', predictions from 'mlr3', or a\nfitted 'caret' model.",
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