evaluation/GROUPED_SPLIT_BENCHMARK.md

Grouped vs pooled split benchmark

This compares the same XGBoost config under two evaluation protocols.

Config: {'n_estimators': 600, 'max_depth': 8, 'learning_rate': 0.1489, 'subsample': 0.9625, 'colsample_bytree': 0.9013, 'reg_alpha': 1.1407, 'reg_lambda': 2.4181, 'eval_metric': 'logloss'} Quick mode: yes (n_estimators=200)

Protocol	Accuracy	F1 (weighted)	ROC-AUC
Pooled random split (70/15/15)	0.9510	0.9507	0.9869
Grouped LOPO (9 folds)	0.8303	0.8304	0.8801

Use grouped LOPO as the primary generalisation metric when reporting model quality.