Table 2. Hyperparameter tuning details for machine learning models.

Algorithm	Parameter	Range of values
Decision tree	criterion	‘gini’, ‘entropy’
	max_depth	6, 8, 10
Random forest	min_samples_leaf	5, 8
	max_depth	6, 8, 10
$k$-Nearest neighbor	n_neighbors	3, 5, 7
	max_depth	5, 7, 10
Gradient boost	min_samples_leaf	5, 8
	max_depth	5, 7, 10
XGBoost	learning_rate	0.01, 0.05, 0.1
CatBoost	learning_rate	0.01, 0.05, 0.1
	depth	5, 7, 9