Table 1.

Fine-tuned XGBoost Parameters

Name and Description	Values
n_estimators: Number of gradient boosted trees.	[50, 100, 200, 500, 1000]
max_depth: Maximum tree depth.	[3, 4, 5, 6, 7]
learning_rate: Boosting learning rate.	[0.01, 0.05, 0.1, 0.2, 0.3]
subsample: Subsample ratio of instances.	[0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
colsample_bytree: Subsample ratio of columns.	[0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
reg_alpha: L1 regularization weights.	[0, 0.1, 1, 5, 10]
reg_lambda: L2 regularization weights.	[0, 0.1, 1, 5, 10]