القائمة
Final modeling once the baseline and feature set are locked: tune against validation, audit overfitting, touch the test set once, justify the complexity.
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
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.
| name | lemma-model |
| description | Final modeling once the baseline and feature set are locked: tune against validation, audit overfitting, touch the test set once, justify the complexity. |
| homepage | https://github.com/tkpratardan/lemma |
| license | MIT |
Provide a comparison against the baseline, the selected model and threshold, stability and subgroup evidence, a locked final evaluation, and operational limitations.
Do not tune against the test set, change metrics mid-search, compare different splits, omit the baseline, or hide subgroup regressions behind an aggregate gain.
For tuning and error-analysis detail, read references/deep-guide.md.
