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lemma-baseline

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更新时间2026年7月15日 00:47

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

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来源

tkpratardan

tkpratardan/lemma

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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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lemma-inference

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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.

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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.