kaggle-tabular-multiclass-s6e7

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.

Create or validate the shared cross-validation split (artifacts/folds.parquet) that every model must use, and check that it tracks the leaderboard. Use before any model trains, or whenever CV and LB diverge.

Scaffold a new tabular-multiclass competition in this kit — download/place data, detect id/target/classes/metric, fill src/config.yaml, pick the fold scheme, and freeze the shared CV split. Use once at the very start, before EDA.

Validate a submission.csv against sample_submission before spending a scarce daily submission slot — column names/order, id coverage, prob ranges/sums, NaNs, and canonical class order. Use right before every leaderboard submission.