---

name: tabular-label-issues description: "Route multi-label classification and regression label-issue workflows for cleanlab." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tabular label issues

Use this sub-skill when the task is about nonstandard tabular labels: multi-label classification with labels as list-of-lists, or regression with numeric targets.

Route here for

• Multi-label issue finding, per-class ranking, and dataset-health helpers.
• Regression CleanLearning workflows and regression label-quality scoring.
• Direct module APIs for these workflows.
• Datalab(task="multilabel") and Datalab(task="regression") only as cross-reference when the user wants the broader audit route.

Route elsewhere

• Standard single-label binary/multiclass noisy-label cleanup -> ../classification/SKILL.md.
• Full dataset audits, lab.find_issues(...), or multiple issue families -> ../datalab/SKILL.md.
• Token, object-detection, or segmentation label issues -> ../structured-label-issues/SKILL.md.
• Multiannotator consensus or annotator-quality workflows -> ../multiannotator/SKILL.md.
• Standalone outlier scoring -> ../outlier/SKILL.md.
• Experimental helpers and span classification -> ../experimental/SKILL.md.

Core contract

• Multilabel labels are List[List[int]] with zero-based class IDs.
• Multilabel pred_probs are np.ndarray with shape (N, K) and need not sum to 1 across rows.
• Use find_label_issues and get_label_quality_scores to flag and rank suspicious multilabel examples.
• Use find_multilabel_issues_per_class when you need per-class issue masks or ranked indices.
• Use rank_classes_by_multilabel_quality, common_multilabel_issues, overall_multilabel_health_score, and multilabel_health_summary for class-level and dataset-level summaries.
• Regression labels/targets are numeric 1D arrays.
• Use cleanlab.regression.rank.get_label_quality_scores for scored numeric predictions and CleanLearning when you want issue detection plus refitting.
• Regression CleanLearning.find_label_issues(...) returns a DataFrame with is_label_issue, label_quality, given_label, and predicted_label.
• Lower scores always mean more suspect labels.

Datalab cross-reference

• Datalab(..., task="multilabel") and Datalab(..., task="regression") use the same tabular label semantics but expose the broader audit router.
• Use the direct module APIs when the only question is label quality.
• Use Datalab when you also need other issue types or a dataset-wide report.
• Datalab issue tables use label_score; direct regression CleanLearning uses label_quality.

Read/run next

• Read references/api-reference.md when you need exact multilabel/regression signatures, return values, and Datalab task parallels.
• Read references/data-formats.md before constructing list-of-lists multilabel inputs or numeric regression targets.
• Read references/multilabel.md when the task is multilabel issue finding, ranking, or dataset health.
• Read references/regression.md when the task is numeric target-quality scoring or regression CleanLearning refitting.
• Read references/troubleshooting.md when shape, label-format, target, scoring, or task-route failures occur.
• Run scripts/smoke_tabular_label_issues.py to verify tiny multilabel and regression paths.