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datalab【数据与研究】Route end-to-end dataset auditing with cleanlab.Datalab.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the user wants a unified dataset audit with cleanlab.Datalab.
Datalab(...), find_issues(...), report(...), get_issues(...), and get_issue_summary(...).image_key and CleanVision.IssueManager workflows registered into Datalab.CleanLearning, count, filter, rank, or dataset-health workflows → ../classification/SKILL.md.../outlier/SKILL.md.../multiannotator/SKILL.md.../tabular-label-issues/SKILL.md.../structured-label-issues/SKILL.md.../experimental/SKILL.md.Datalab accepts task="classification", task="regression", and task="multilabel".find_issues() accepts pred_probs, features, knn_graph, and issue_types.knn_graph wins when both knn_graph and features are provided.issue_types=None means use the task default set; issue_types={} means do nothing.report() prints to stdout and does not return a report string.get_issue_summary() is the dataset-level view; get_issues() is the per-example view.spurious_correlations is image-only and lives in get_info("spurious_correlations"), not in issue_summary.references/api-reference.md when you need live-verified constructor/method signatures, task values, or output table meanings.references/issue-types.md when deciding which issue families and inputs to request in find_issues().references/workflows.md when you need complete classification, regression, multilabel, kNN-graph, image, serialization, or report recipes.references/custom-issue-manager.md before registering a custom issue type.references/troubleshooting.md when dependencies, task names, issue_types, labels, images, or reports fail.scripts/smoke_datalab.py to verify a tiny Datalab audit and custom issue-manager path.