Table 4. A detailed analysis of two hyperparameters considered in the algorithms of this study (Adaboost, Random Forest, SVM, XGBoost and logistic regression).

Hyperparameters are values that define the behavior of the machine learning algorithm and directly influence its performance.

Algorithm	Hyperparameters
Adaboost	depth = {“max depth”: [1,2,3],
	learning rate = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
	‘n estimators’: [50, 100, 150, 200],
	‘min samples leaf’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}
Random Forest	depth = {“max depth”: [3,4, 5, 6, 7, 8, 9, 10],
	“min samples split”: [2, 4, 8, 12, 16],
	‘n estimators’: [50, 100, 150],
	‘criterion’: [gini, entropy],
	‘max features’: [auto, 2, 3, 4, 6, 8, 10,11],
	‘min samples leaf’: [2, 3, 4, 5, 6, 7, 8]
XGBoost	depth = {[3, 4, 5, 6],
	‘learning_rate’: [0.01, 0.05, 0.1, 0.2],
	‘n_estimators’: [50, 100, 150, 200],
	‘min_child_weight’: [1, 2, 3, 4],
	‘subsample’: [0.8, 0.9, 1],
	‘colsample_bytree’: [0.8, 0.9, 1]}
Logistic Regression	{‘penalty’: [‘l1’, ‘l2’, ‘elasticnet’, ‘none’],
	‘C’: [0.001, 0.01, 0.1, 1, 10],
	‘solver’: [‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’],
	‘max_iter’: [50, 100, 200]}