Table 1 Hyperparameters evaluated for the machine learning models

Logistic Regression

solver: lbfgs

max_iter: 250

penalty: l2

C: [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]

Naive Bayes

–

-

kNN

algorithm: ball_tree

leaf_size: 50

n_neighbors: [1,2,3,4,5,6,7,8,9]

p: [1,2]

SVC

–

for kernels: [rbf, poly, sigmoid]

C: [−4, −3, −2, −1, 0, 1, 2, 3]

for kernel: linear

gamma: [0.00001, 0.0001, 0.001, 0.01, 0.1]

C: [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]

Decision Trees

–

max_depth: [1,2,3,4,5,6,7,8,9]

criterion: [gini, entropy]

Random Forest

–

max_depth: [3,4,5,8,10]

n_estimators: [5, 20, 50, 100, 200, 500, 1000]

Gradient Boosting

–

max_depth: [3,4,5,8,10]

leargning_rate: [0.01, 0.05, 0.1, 0.2]

n_estimators: [5, 20, 50, 100, 200, 500, 1000]

XGBoost

–

max_depth: [6,7,8]

leargning_rate: [0.01, 0.025, 0.05, 0.075, 0.1]

n_estimators: [5, 20, 50, 100, 200, 500, 1000]

AdaBoost

–

learning_rate: [0.25, 0.5, 1.0, 1.25, 1.5]

n_estimators: [20, 50, 100, 150, 200]