kaggle-blend

name	kaggle-blend
description	Combine all model OOF predictions into the best validated blend/stack, apply the frozen recipe to test predictions, and write submission.csv. Use after the model agents finish, or any time the OOF pool changes.

Skill: blend / stack the model pool

Turn the pool of per-model OOF + test predictions into one submission that beats every single model — validated on the shared folds, never on the leaderboard.

Run it

python templates/blend.py            # discovers all artifacts/oof/*.parquet, writes submission.csv

What it does (and what to check)

1. Discover every artifacts/oof/<name>.parquet and its matching test_preds. Align to the train target by id.
2. Diagnose — print each model's OOF CV and the pairwise correlation matrix. Redundant members (corr ≈ 0.99) add nothing; diverse ones (a decorrelated MLP) lift the blend.
3. Search on OOF, simplest first:
   • hill-climb weighted average (greedy add-with-replacement) — the robust default;
   • rank / geometric-mean average — for similarly-miscalibrated models;
   • a regularized stack (multinomial LR / shallow GBM) on the OOF prob matrix with the same folds — keep only if it beats the plain blend on OOF beyond fold noise.
4. Freeze & apply — the winning weights/meta-model are frozen and applied to the test prediction matrix (same members, same order). Blend weights come from OOF, never from LB.
5. Metric-aware post-processing (last): clip for logloss; OOF-tuned argmax thresholds / class priors for accuracy or macro-F1; OOF cut points for QWK. Verify each helps on OOF first.
6. Write submission.csv, then run the kaggle-submit-check skill.

Guardrails

• Add a member only if it improves OOF CV. Don't over-blend; simpler generalizes better.
• Blend on OOF, apply identically to test. If you ever fit weights on the LB, you've overfit it.
• Record the recipe + member weights + final CV in reports/BLEND.md and the scoreboard.