Table 2 Hyperparameter settings of the proposed ML methods.
Model | Parameter | Value |
|---|---|---|
RF | Bagged Tree | – |
Number of Learners | 30 | |
Minimum Leaf Size | 8 | |
Number of Predictors to Sample | Select all | |
GB | Feature Selection | 13/13 individual features selected |
Boosted Tree | – | |
Number of Learners | 30 | |
Minimum Leaf Size | 8 | |
Number of Predictors to Sample | Select all | |
Learning Rate | 0.1 | |
SVR | Regularization Parameter | 1000 |
Kernel Function | Cubic | |
Kernel Coefficient | 1 | |
Feature Selection | 13/13 individual features selected | |
ANN | Activation Function | ReLU |
Alpha | 0.005 | |
Iteration Limit | 1000 | |
Feature Selection | 13/13 individual features selected | |
GPR | Basic Function | Constant |
Use Isotropic Kernel | Yes | |
Signal Standard Deviation | Automatic | |
Sigma | Automatic |
