---

name: outlier description: "Route cleanlab outlier and OOD scoring from features or pred_probs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Outlier

Use this sub-skill for direct cleanlab.outlier.OutOfDistribution workflows.

Route here when

• You have numeric feature embeddings and want KNN-based outlier scores.
• You have classifier pred_probs and want uncertainty-based outlier scores.
• You need to rank the most atypical points from a score vector.

Route elsewhere

• Broader dataset audits, lab.find_issues(...), or get_issues("outlier") in a mixed audit -> ../datalab/SKILL.md.
• Standard noisy-label or dataset-health workflows -> ../classification/SKILL.md.
• Multiannotator workflows -> ../multiannotator/SKILL.md.
• Multi-label/regression or structured-output label issues -> ../tabular-label-issues/SKILL.md or ../structured-label-issues/SKILL.md.

Core contract

• Constructor: OutOfDistribution(params: Optional[dict] = None) -> None
• Public methods: fit, score, fit_score
• Lower scores mean more atypical examples.
• Rank worst scores with cleanlab.rank.find_top_issues.

Cross-links

• Datalab uses the same outlier ideas when you want a broader audit.
• cleanlab.rank.find_top_issues is the shared ranking helper used by classification workflows too.

Read/run next

• Read API reference when you need constructor, method, parameter, and return-shape details.
• Read Workflows when choosing feature-based versus pred_probs-based scoring or using Datalab as a wrapper.
• Read Troubleshooting when shape, label, metric, fitting, or interpretation errors arise.
• Run Smoke helper to verify tiny feature-based and pred_probs-based outlier cases.