Table 1 Training set for the decision tree machine learning model

Naive Bayes

var_smoothing

1e-09

Logistic Regression

C

0.01

penalty

l2

Support Vector Machine

C

1

Kernel

1

Gamma

rbf

Decision Tree

criterion

Gini

max_depth

None

min_samples_leaf

1

min_samples_split

2

Random Forest

criterion

entropy

max_depth

None

min_samples_leaf

1

min_samples_split

2

n_estimators

15

Multilayer Perceptron

alpha

0.001

hidden_layer_sizes

100,

learning_rate

constant

max_iter

1000