Table 1 Hyperparameter ranges for each classifier

Logistic Regression with elastic net penalty

C

[0.01, 2]

L1-ratio

[0.01, 0.99]

Decision Tree

max_depth

[10, 100, step=10]

min_samples_leaf

(1, 2, 4)

min_samples_split

(2, 5, 10)

Random Forest

n_estimators

[600, 1400, step=200]

max_depth

[10, 100, step=10]

min_samples_leaf

(1, 2, 4)

min_samples_split

(2, 5, 10)

XGBoost

eta

[0.01, 0.7]

gamma

[0, 0.5, step=0.1]

max_depth

[1, 22, step=1]

n_estimators

[50, 400, step=25]