تشغيل أي مهارة في Manus بنقرة واحدة

lemma-baseline

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التفرعات١

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Establish an honest score to beat before complex modeling, including the validation design, metric, no-information rule, and simplest credible model.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

tkpratardan

tkpratardan/lemma

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

علماء البياناتمهن الحاسوب والرياضيات·SOC 15-2051

مستكشف الملفات

2 ملفات

SKILL.md

readonly

name	lemma-baseline
description	Establish an honest score to beat before complex modeling, including the validation design, metric, no-information rule, and simplest credible model.

Baseline

Deliver

Provide a reproducible validation design, a no-information or rule baseline, the simplest credible model, and the precise score later models must beat.

Check

Define the outcome, observation unit, prediction moment, intended use, and a fixed seed.
Match the split to deployment: time-ordered for temporal prediction, grouped for repeated entities, otherwise a fixed random split.
Keep preprocessing inside the training pipeline.
Choose a decision-relevant metric; do not rely on accuracy alone for imbalanced outcomes.
Compare subgroup behavior and variation across folds or seeds when material.

Do not tune complex models, repeatedly inspect a final test set, preprocess before splitting, or celebrate a score without the dumb baseline.

For feature iteration and metric guidance, read references/deep-guide.md.

المزيد من هذا المستودع

نفس المستودع

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-leakage

tkpratardan/lemma

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

2026-07-154

name	lemma-baseline
description	Establish an honest score to beat before complex modeling, including the validation design, metric, no-information rule, and simplest credible model.

Baseline

Deliver

Provide a reproducible validation design, a no-information or rule baseline, the simplest credible model, and the precise score later models must beat.

Check

Define the outcome, observation unit, prediction moment, intended use, and a fixed seed.
Match the split to deployment: time-ordered for temporal prediction, grouped for repeated entities, otherwise a fixed random split.
Keep preprocessing inside the training pipeline.
Choose a decision-relevant metric; do not rely on accuracy alone for imbalanced outcomes.
Compare subgroup behavior and variation across folds or seeds when material.

Do not tune complex models, repeatedly inspect a final test set, preprocess before splitting, or celebrate a score without the dumb baseline.

For feature iteration and metric guidance, read references/deep-guide.md.