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Ejecuta cualquier Skill en Manus con un clic

lemma-leakage

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Actualizado15 de julio de 2026 a las 00:47

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

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tkpratardan

tkpratardan/lemma

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

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SKILL.md

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name	lemma-leakage
description	Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.
homepage	https://github.com/tkpratardan/lemma
license	MIT

Leakage audit

Deliver

Rank leakage findings by severity and show reproducible before/after scores, the contaminated path, the corrected validation design, and remaining risk.

Check

Reconstruct information available at the real prediction moment.
Audit features and missingness for target proxies and post-outcome facts.
Verify that imputers, encoders, selection, and scaling are fit within each training fold.
Check time travel, repeated entities, near duplicates, source overlap, and outcome-dependent sampling.
Remove suspicious features or strengthen the split and compare with a simple baseline.

Do not rationalize an implausible score, rely only on feature importance, randomly split temporal or repeated-entity data, or continue tuning before material leakage is resolved.

For the full leakage taxonomy, read references/deep-guide.md.

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name	lemma-leakage
description	Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.
homepage	https://github.com/tkpratardan/lemma
license	MIT

Leakage audit

Deliver

Rank leakage findings by severity and show reproducible before/after scores, the contaminated path, the corrected validation design, and remaining risk.

Check

Reconstruct information available at the real prediction moment.
Audit features and missingness for target proxies and post-outcome facts.
Verify that imputers, encoders, selection, and scaling are fit within each training fold.
Check time travel, repeated entities, near duplicates, source overlap, and outcome-dependent sampling.
Remove suspicious features or strengthen the split and compare with a simple baseline.

Do not rationalize an implausible score, rely only on feature importance, randomly split temporal or repeated-entity data, or continue tuning before material leakage is resolved.

For the full leakage taxonomy, read references/deep-guide.md.