Table 4.

Overview of input Parameter grid

Machine learning algorithm	Parameter grid
Random Forest	∙ ‘max_depth’: 10,150,500,1000
	∙ ‘max_features’: 30,500,3000
	∙ ‘min_samples_leaf’: 1,10,100
	∙ ‘min_samples_split’: 2,10,100
	∙ ‘n_estimators’: 10, 100
Logistic Regression	∙ ‘random_state’: 0
K-Nearest-Neighbors	∙ ‘n_neighbors’: 3
SVC	∙ ‘gamma’: 2
	∙ ‘C’: 0.025, 1
	∙ ‘kernel’: linear
Decision Tree	∙ ‘max_depth’: 5, 10, 15
Multi-layer Perceptron	∙ ‘alpha’: 1
	∙ ‘max_iter’: 1000
Neural Network (BERT)	∙ ‘max_length’: 256
	∙ ‘epochs’: 5
	∙ ‘lr’: 1e-5
	∙ ‘eps’: 1e-8