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Ejecuta cualquier Skill en Manus con un clic

lemma-baseline

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Actualizado15 de julio de 2026 a las 00:47

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

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tkpratardan

tkpratardan/lemma

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

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SKILL.md

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name	lemma-baseline
description	Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.
homepage	https://github.com/tkpratardan/lemma
license	MIT

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.

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name	lemma-baseline
description	Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.
homepage	https://github.com/tkpratardan/lemma
license	MIT

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.