Table 5.

Hyperparameters evaluated in the algorithms.

Algorithm	Hyperparameters
Decision tree	depth= {“max_depth”:[3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], “min_samples_split”:[2, 4, 8, 12, 16], “min_samples_leaf”: [2, 3, 4, 5, 6, 7, 8], “criterion”: [ ‘gini’, ‘entropy’] }
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]}
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]}