Table 2 Optimized hyperparameter settings of various regression models, including Random Forest (RF), K-Nearest Neighbors (KNN), AdaBoost, Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Decision Tree (DT), and Extreme Gradient Boosting(XGBoost), aimed at enhancing model performance.
Model | Hyperparameter | Value | Model | Hyperparameter | Value |
|---|---|---|---|---|---|
RF | n_estimators | 424 | SVR | kernel | RBF |
| Â | max_depth | 15 | Â | C | 20 |
KNN | n_neighbors | 5 | Â | epsilon | 0.2 |
AdaBoost | n_neighbors | 440 | DT | max_depth | 5 |
GBR | n_estimators | 224 | XGBoost | max_depth | 5 |
| Â | learning_rate | 0.1 | Â | learning_rate | 0.06 |
| Â | max_depth | 5 | Â | n_estimators | 424 |
