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) : 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) 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.