Table 1. Hyperparameter tuning for machine learning algorithms.

Classifier	Hyperparameters and Values
Random Forest	Hyperparameters set by default using the gini criterion.
Extra Tree	Hyperparameters set by default using the gini criterion.
Gradient Boosting Machine	Criterion = ’friedman_mse’ max_depth = 1 min_samples_leaf = 2 min_weight_fraction_leaf = 0.1 presort = ’deprecated’
Soft Voting Ensemble Classifier	estimators = [RandomForestClassifier(),ExtraTreesClassifier(),GradientBoostingClassifier(criterion = ’friedman_mse’, max_depth = 1, min_samples_leaf = 2, min_weight_fraction_leaf = 0.1, presort = ’deprecated’)] voting = ’soft’ weights = [9, 6, 2]