원클릭으로 Manus에서 모든 스킬 실행

lemma-leakage

스타4

포크1

업데이트2026년 7월 15일 00:47

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

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

tkpratardan

tkpratardan/lemma

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

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.

이 저장소의 다른 Skills

같은 저장소

lemma-baseline

tkpratardan/lemma

Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.

2026-07-154

lemma-causal

tkpratardan/lemma

Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.

2026-07-154

lemma-describe

tkpratardan/lemma

Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.

2026-07-154

lemma-eda

tkpratardan/lemma

EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.

2026-07-154

lemma-inference

tkpratardan/lemma

Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.

2026-07-154

lemma-model

tkpratardan/lemma

Final modeling once the baseline and feature set are locked: tune against validation, audit overfitting, touch the test set once, justify the complexity.

2026-07-154

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.