Table 6 Hyperparameters of ML models.
Model | Hyperparameter | Range/Method Used | Final Value | Remarks |
|---|---|---|---|---|
SVM | C | [0.1, 1, 10, 100] | 1 | Balanced regularization and margin |
kernel | [‘linear’, ‘rbf’, ‘poly’] | ‘rbf’ | Captured nonlinear patterns | |
gamma | [‘scale’, ‘auto’, 0.1, 1] | ‘scale’ | Automated feature scaling | |
AdaBoost | n_estimators | [50, 100, 200, 500] | 200 | Optimal trade-off between speed and accuracy |
learning_rate | [0.01, 0.1, 1] | 0.1 | Best convergence observed | |
Random Forest | n_estimators | [50, 100, 200] | 100 | Balanced computational cost and accuracy |
max_depth | [5, 10, 20] | 10 | Prevented overfitting | |
min_samples_split | [2, 5, 10] | 5 | Improved split efficiency |
