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lemma-eda

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Mis à jour15 juillet 2026 à 00:47

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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Source

tkpratardan

tkpratardan/lemma

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Scientifiques des donnéesProfessions informatiques et mathématiques·SOC 15-2051

Explorateur de fichiers

3 fichiers

SKILL.md

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name	lemma-eda
description	EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.
homepage	https://github.com/tkpratardan/lemma
license	MIT

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.

Plus depuis ce dépôt

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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-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

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-eda
description	EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.
homepage	https://github.com/tkpratardan/lemma
license	MIT

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.