Find Label Errors in Classification Data with Confident Learning
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Documentation for package ‘labelnoise’ version 0.1.0
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| class_thresholds | Per-class confidence thresholds |
| confident_joint | The confident joint |
| estimate_joint | Estimate the joint distribution of given and true labels |
| find_label_issues | Find examples with likely label errors |
| health_summary | Summarise label health by class |
| label_inputs | Standardise labels and predicted probabilities |
| label_inputs.data.frame | Standardise labels and predicted probabilities |
| label_inputs.default | Standardise labels and predicted probabilities |
| label_inputs.labelnoise_inputs | Standardise labels and predicted probabilities |
| label_inputs.PredictionClassif | Standardise labels and predicted probabilities |
| label_inputs.ResampleResult | Standardise labels and predicted probabilities |
| label_inputs.train | Standardise labels and predicted probabilities |
| label_inputs.tune_results | Standardise labels and predicted probabilities |
| label_quality_scores | Label quality scores |
| noise_matrices | Noise transition matrices and class priors |
| num_label_issues | Estimate the number of label errors |
| simulate_label_noise | Simulate a classification dataset with label errors |