Table 9. Parameters used for comparison of Decision Tree, Random Forest, KNN, AdaBoost, MLP, and SVM.

Classifier	Parameters
Decision Tree	criterion=‘entropy’, splitter=‘best’, random_state = 0, min_samples_split = 2
Random Forest	criterion=‘entropy’, n_estimators = 10, random_state = 0
KNN	leaf_size = 30, metric=‘minkowski’, n_neighbors = 5, p = 2, weights=‘uniform’
AdaBoost	n_estimators = 50, random_state = None
MLP	activation=‘relu’, alpha = 0.0001, max_fun = 15000, max_iter = 50, random_state = 0, solver=‘adam’
SVM	estimator=‘LinearSV’, max-iter=[500, 1000], C=[0.1,1]