The full narrative is unavailable.
The original evidence text could not be loaded. Use the source record below; availability of linked files is checked separately.
Open the original HTML fileAll findingsPublished research
Voidly ResearchPublished
Forecast hyperparameter grid search: defaults already near-optimal
Ran a 27-cell GridSearchCV over XGBoost (n_estimators × max_depth × learning_rate) plus a follow-up min_child_weight/gamma sweep. Holdout AUC improved +0.007. But LOCO median AUC DROPPED -0.003. Best params lose in 7 of 10 most-active countries. The current defaults are at the practical ceiling for…
Read the full published summary
Ran a 27-cell GridSearchCV over XGBoost (n_estimators × max_depth × learning_rate) plus a follow-up min_child_weight/gamma sweep. Holdout AUC improved +0.007. But LOCO median AUC DROPPED -0.003. Best params lose in 7 of 10 most-active countries. The current defaults are at the practical ceiling for this feature set. Future gains require feature engineering, not hyperparams.
The source record.
Original source links and labels. Linked responses may have changed since publication.