lemma-model

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.