Table 2 Hyperparameter search ranges and optimal configurations for each model.
From: Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS
Model | Hyperparameter | Search Range | Best Value |
|---|---|---|---|
ET | n_estimators | [50, 100, 150, 200] | 50 |
|  | criterion | [‘gini’, ‘entropy’] | gini |
RF | n_estimators | [100, 150, 200] | 150 |
|  | criterion | [‘gini’, ‘entropy’] | entropy |
DT | splitter | [‘best’, ‘random’] | random |
|  | criterion | [‘gini’, ‘entropy’] | entropy |
BC | n_estimators | [10, 50, 100] | 50 |
| Â | max_samples | [0.5, 0.7, 1.0] | 1.0 |
AdaBoost | n_estimators | [50, 100, 150] | 100 |
| Â | learning_rate | [0.01, 0.1, 0.5, 1.0] | 0.01 |
KNN | n_neighbors | [5, 10, 20, 30, 40] | 30 |
|  | weights | [‘uniform’, ‘distance’] | distance |
