# 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.