Data Rigor & Leakage Audit

Wrong splits invalidate every downstream number. Do this before training and re-check whenever results look too good.

Leakage checklist

• Group leakage (most common). If one entity (patient, person, product, document, study) yields multiple samples, ALL its samples must sit in ONE split. Random per-sample splitting scatters them across train/test the model memorizes the entity. Use GroupShuffleSplit / StratifiedGroupKFold keyed by the entity id.
• Temporal leakage. Time-series / any time-ordered data: train must be strictly earlier than val/test. No shuffling across time. No future-derived features.
• Official split. If the benchmark defines a train/test split (e.g., encoded in an ID), use it - otherwise numbers aren't comparable to papers.
• Preprocessing / normalization leakage. Fit scalers, PCA, class weights, augmentation stats, and target encodings on train only, then apply to val/test. Never fit on the full set.
• Duplicate / near-duplicate leakage. De-duplicate (and augmented copies) across splits.
• Target leakage in features. Drop features that encode the label or are only known post-outcome.
• Threshold/selection leakage. Choosing a decision threshold or doing model selection using test labels is leakage. Do it on validation.

Make it enforceable

Add explicit assertions that split membership is disjoint at the entity level, e.g.:

assert not (train_ids & val_ids) and not (train_ids & test_ids) and not (val_ids & test_ids)

Print per-split counts of entities (not just samples) and the class balance.

Also verify

• Labels are correct and consistent (one authoritative source; handle multi-label-per-entity deliberately).
• Class balance - decide handling now (class-weighted loss OR resampling, not both by default).
• Manifest, not copies. Prefer a manifest table (path, entity, label, split) over shuffling files into folders - it makes the split auditable.