Table 4.

Hyperparameters tuning for the classification models using the Bayesian Optimization approach.

Models	Best Hyperparameters
RF	N_estimators = 10, criterion = gini.
RC	Alpha = 0.4, copy_X = false, fit_intercept = true, normalize = false, solver = lsqr, tol = 0.01.
DT	Criterion = entropy, splitter = random.
NB	Alpha = 0.1, var_smoothing = 0.00001.
LR	Penalty = l2, solver = lbfgs.
SVM	Kernel = rbf, regularization parameter (C) = 0.4.