Table 5. Hyperparameter settings for machine learning algorithms.

Algorithm	Hyperparameter	Tuning Range
RF	n_estimators = 200, max_depth = 50	n_estimators={20 to 200}, max_depth={5 to 100}
GBM	max_depth = 200, learning_rate = 0.2, n_estimators = 50, random_state = 52	n_estimators={20 to 200}, max_depth={5 to 100}, random_state={2 to 100 }, learning_rate= {0.1 to 0.9 }
AdaBoost	n_estimators = 300, random_state = 5, learning_rate = 0.8	n_estimators={20 to 200}, random_state={2 to 100 }, learning_rate= {0.1 to 0.9 }
LR	solver=‘saga’, multi_class=‘multinomial’, C = 3.0	solver= [‘saga’,‘sag’,‘liblinear’], multi_class=‘multinomial’, C={1.0 to 5.0}
SVC	kernel=‘linear’, C = 1.0	kernel=[‘linear’, ‘poly’, ‘sigmoid’], C={1.0 to 5.0}