Table 4.

Models and their hyperparameters.

Models	Hyperparameters
Neural Network	MLPClassifier (alpha=1e-05, hidden_layer_sizes=100, random_state=42)
Gradient Boosting Decision Tree	GradientBoostingClassifier (max_depth=1, max_features=‘auto’, min_samples_leaf=186, min_samples_split=179, n_estimators=102, random_state=42)
eXGBoosting Machine	XGBClassifier (base_score=0.5, booster=‘gbtree’, colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints=‘‘, learning_rate=0.125, max_delta_step=0, max_depth=75, min_child_weight=56, missing=nan, monotone_constraints=‘()’, n_estimators=36, n_jobs=8, num_parallel_tree=1, predictor=‘auto’, random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=‘exact’, use_label_encoder=False, validate_parameters=1, verbosity=None)
Decision Tree	DecisionTreeClassifier (max_depth=24, max_features=‘auto’, min_samples_leaf=100, min_samples_split=173, random_state=42)
Support Vector Machine	SVC (C=0.09837555188414593, gamma=0.11638567021515211, probability=True)