القائمة
Audit suspicious model performance or a pipeline for target, preprocessing, temporal, group, sampling, or duplicate contamination.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | lemma-leakage |
| description | Audit suspicious model performance or a pipeline for target, preprocessing, temporal, group, sampling, or duplicate contamination. |
Rank leakage findings by severity and show reproducible before/after scores, the contaminated path, the corrected validation design, and remaining risk.
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.
Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.
Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.
Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.
EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.
Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.
Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.
