Models Parameters Explanation Values
RF criterion measurement methods of impurities gini
n_estimators number of trees in the forest 110
max_depth the maximum depth of the tree 5
max_features maximum number of retained features when branching 10
GBDT n_estimators number of trees in the forest 45
max_depth the maximum depth of the tree 2
min_samples_split the number of training samples at least included in the sub-node 2
learning_rate Learning rate 0.1
Decision Tree criterion measurement methods of impurities entropy
random_state parameters of random patterns in branches 13
splitter way of the tree branches random
max_depth the maximum depth of the tree 5
KNN n_neighbors number of nearest neighbor samples with voting rights 5
weights voting proportion method uniform
Naïve Bayes alpha Laplacian smoothing coefficient 20