원클릭으로 Manus에서 모든 스킬 실행

lemma-model

스타4

포크1

업데이트2026년 7월 15일 00:47

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

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

Final modeling

Deliver

Provide a comparison against the baseline, the selected model and threshold, stability and subgroup evidence, a locked final evaluation, and operational limitations.

Check

Keep one validation design and decision metric across candidates.
Compare a small motivated model set; keep preprocessing and tuning within training data and set fixed seeds.
Diagnose train-validation gaps, fold or temporal stability, calibration, subgroup behavior, latency, and interpretability.
Challenge the most fragile feature or assumption with an ablation, shifted window, or leakage audit.
Lock the pipeline and threshold before touching the final test set once.

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.

이 저장소의 다른 Skills

같은 저장소

lemma-baseline

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

2026-07-154

lemma-causal

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

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lemma-describe

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

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lemma-eda

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

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lemma-inference

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

2026-07-154

lemma-leakage

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

2026-07-154

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

Final modeling

Deliver

Provide a comparison against the baseline, the selected model and threshold, stability and subgroup evidence, a locked final evaluation, and operational limitations.

Check

Keep one validation design and decision metric across candidates.
Compare a small motivated model set; keep preprocessing and tuning within training data and set fixed seeds.
Diagnose train-validation gaps, fold or temporal stability, calibration, subgroup behavior, latency, and interpretability.
Challenge the most fragile feature or assumption with an ablation, shifted window, or leakage audit.
Lock the pipeline and threshold before touching the final test set once.

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.