| Random forest [23] |
The depth of the tree (T), number of tree models (N) |
T = 3, N = 100 |
|
| Logistic regression [22] |
Confidence factor used for pruning (C), class weight adjustment (class weight), maximum iteration (max_iter) |
C = 1.0, class weight = None, dual = False, max_iter = 100 |
|
| Decision tree [22] |
Confidence factor used for pruning (C), minimum number of instances of each leaf (N) |
C = 0.25, N = 2 |
|
| K-nearest neighbors [22] |
Number of neighbors (n_neighbors), weight function used in prediction (weights) |
n_neighbors = 5, weights = uniform |
|
| Support vector machine [22] |
Confidence factor used for pruning (C), kernel type (kernel); maximum iteration(max_iter) |
C = 1.0, kernel = “linear”, max_iter = 100 |
|
| XGBoost [22] |
Depth of the tree (T), learning rate, number of estimators, gamma, and several tuning parameters |
T = 3, learning rate = 0.1, number of estimators = 100 |
