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lemma-unsupervised

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Mis à jour15 juillet 2026 à 00:47

Discover or evaluate structure without labels, including clustering, anomaly detection, embeddings, dimensionality reduction, and topic models.

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tkpratardan

tkpratardan/lemma

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Scientifiques des donnéesProfessions informatiques et mathématiques·SOC 15-2051

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SKILL.md

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name	lemma-unsupervised
description	Discover or evaluate structure without labels, including clustering, anomaly detection, embeddings, dimensionality reduction, and topic models.

Unsupervised analysis

Deliver

Provide the discovered structure or anomalies, the preprocessing and selection choices that produced them, stability evidence, useful interpretation, and clear limits on what the pattern means.

Check

Define the observation unit, intended use, distance or similarity notion, and seed.
Inspect scaling, missingness, redundancy, outliers, and high-cardinality features before fitting.
Compare against a simple reference and a small motivated set of representations or algorithms.
Test stability across resamples, seeds, perturbations, or time windows.
Evaluate usefulness with domain checks or downstream outcomes that were not used to manufacture the pattern.

Do not treat an attractive projection as validation, select cluster count from one index alone, or assign causal or essential meaning to algorithmic groups.

For stability and interpretation patterns, read references/deep-guide.md.

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Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.

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name	lemma-unsupervised
description	Discover or evaluate structure without labels, including clustering, anomaly detection, embeddings, dimensionality reduction, and topic models.

Unsupervised analysis

Deliver

Provide the discovered structure or anomalies, the preprocessing and selection choices that produced them, stability evidence, useful interpretation, and clear limits on what the pattern means.

Check

Define the observation unit, intended use, distance or similarity notion, and seed.
Inspect scaling, missingness, redundancy, outliers, and high-cardinality features before fitting.
Compare against a simple reference and a small motivated set of representations or algorithms.
Test stability across resamples, seeds, perturbations, or time windows.
Evaluate usefulness with domain checks or downstream outcomes that were not used to manufacture the pattern.

Do not treat an attractive projection as validation, select cluster count from one index alone, or assign causal or essential meaning to algorithmic groups.

For stability and interpretation patterns, read references/deep-guide.md.