Table 2.

Optimal parameters for the classification models.

Classifier	Parameters
KNeighborsClassifier	KNeighborsClassifier(n_neighbors = 20)
DecisionTreeClassifier	DecisionTreeClassifier(max_depth = 90, max_features = ’log2’, max_leaf_nodes = 30, min_samples_leaf = 12, min_weight_fraction_leaf = 0.2)
RandomForestClassifier	RandomForestClassifier(max_depth = 50, max_features = ’auto’, max_leaf_nodes = 40, min_samples_leaf = 3, random_state = 0)
SVC	SVC(C = 5.0, gamma = ’auto’, tol = 0.1)
GradientBoostingClassifier	GradientBoostingClassifier(learning_rate = 0.02, max_depth = 15, min_samples_split = 5, n_estimators = 1000)