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lemma-model

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更新日2026年7月15日 00:47

Select and evaluate a production-worthy model after an honest baseline and validation design exist; use for tuning, calibration, thresholding, and final evaluation.

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tkpratardan

tkpratardan/lemma

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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.

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lemma-describe

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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.

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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-model
description	Select and evaluate a production-worthy model after an honest baseline and validation design exist; use for tuning, calibration, thresholding, and final evaluation.

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.