Table 2.

Full super-parameters of techniques.

Model and Optimal Parameters

DT: 'ccp_alpha': 0.0, 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_split': 20 RF: n_estimators = 100 , max_features = 2 XGBoost: {'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 50, 'subsample': 0.6} LightGBM: {'colsample_bytree': 0.8, 'learning_rate': 0.2, 'n_estimators': 100, 'num_leaves': 31, 'subsample': 0.6} SVM:{'C': 0.1, 'degree': 2, 'gamma': 'scale', 'kernel': 'linear'} ANN: {'activation': 'logistic', 'hidden_layer_sizes': (100,)}