## 4 Model Training and Evaluation Methods
### Hyperparameter Optimization with GA
\begin{table}
\begin{tabular}{p{113.8 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
**Metrics** & **MLModels** & **DT** & **RF** & **K-NNs** & **AdaBoost** & **GBM** & **ETR** & **BR** & **SVR** & **HGBR** & **XGBoost** \\ \hline R\({}^{2}\) & 0.896301 & 0.929128 & 0.913097 & 0.928929 & 0.944055 & 0.924588 & 0.108244 & 0.935501 & 0.748037 & 0.944935 \\ \hline MSE & 0.085752 & 0.058607 & 0.071863 & 0.058771 & 0.046263 & 0.062361 & 0.737422 & 0.053336 & 0.208357 & 0.045535 \\ \hline RMSE & 0.292834 & 0.242088 & 0.268072 & 0.242427 & 0.215088 & 0.249722 & 0.858733 & 0.230946 & 0.456461 & 0.213389 \\ \hline MAE & 0.232702 & 0.187643 & 0.212346 & 0.193578 & 0.16805 & 0.195663 & 0.720991 & 0.182679 & 0.35861 & 0.166036 \\ \hline MSLE & 0.01452 & 0.010516 & 0.012664 & 0.010215 & 0.008234 & 0.010839 & 0.109325 & 0.009929 & 0.037209 & 0.007811 \\ \hline Med AE & 0.193143 & 0.150683 & 0.183506 & 0.168019 & 0.134978 & 0.156779 & 0.632939 & 0.150914 & 0.287834 & 0.140217 \\ \hline MaxEr & 0.969728 & 0.88688 & 1.007571 & 0.800483 & 0.867643 & 0.867454 & 0.23398 & 0.823983 & 0.8236151 & 0.873608 \\ \hline ExVar & 0.98632 & 0.929128 & 0.913848 & 0.92909 & 0.944072 & 0.924598 & 0.108613 & 0.935531 & 0.748241 & 0.944945 \\ \hline MARE & 0.401476 & 0.385017 & 0.430512 & 0.399119 & 0.31794 & 0.395242 & 1.620936 & 0.35992 & 0.959208 & 0.326581 \\ \hline MSRE & 7.311951 & 8.852349 & 11.71123 & 10.6093 & 5.538571 & 0.90187 & 148.025 & 9.051076 & 62.08498 & 7.247817 \\ \hline RMSRE & 2.704062 & 2.97529 & 3.422168 & 3.257192 & 2.353417 & 3.015273 & 12.16655 & 3.008501 & 7.879402 & 2.692177 \\ \hline RRMSE & 1.40364 & 1.54443 & 1.776398 & 1.690762 & 1.221625 & 1.565185 & 6.315483 & 1.56167 & 4.090085 & 1.397471 \\ \hline ME & \(-0.00401\) & \(-0.00289\) & \(-0.02491\) & \(-0.01153\) & 0.003782 & 0.002897 & \(-0.01745\) & 0.004974 & \(-0.01301\) & 0.00278 \\ \hline erMAX & 42.44329 & 0.85.84818 & 63.27024 & 52.46861 & 37.14585 & 46.29575 & 180.5393 & 60.64751 & 118.0525 & 48.97678 \\ \hline SD & 0.292807 & 0.242071 & 0.266912 & 0.242152 & 0.215055 & 0.249705 & 0.8585855 & 0.230892 & 0.456276 & 0.213371 \\ \hline t-stat & \(-0.29941\) & \(-0.26152\) & \(-0.24235\) & \(-1.04245\) & 0.384999 & 0.253934 & \(-0.44494\) & 0.474194 & \(-0.62402\) & 0.285113 \\ \hline U\({}_{55}\) & 0.026222 & 0.021679 & 0.023903 & 0.021686 & 0.019259 & 0.022362 & 0.076888 & 0.020677 & 0.040862 & 0.019108 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metrics results of the ML models, with hyperparameters selected by GA. \begin{table}
\begin{tabular}{p{113.8 pt} p{28.5 pt}} \hline \hline
**Models** & **Hyperparameters and Associated Values** \\ \hline DT & criterion = absolute\_error, max\_depth = 10, max\_leaf\_nodes = 362, min\_samples\_leaf = 7, random\_state = 42 \\ \hline RF & n\_estimators = 600, criterion = squared\_error, max\_depth = 95, min\_samples\_leaf = 2, random\_state = 42 \\ \hline K-NNs & n\_neighbors = 13, weights = uniform, metric = manhattan, leaf\_size = 90 \\ \hline AdaBoost & n\_estimators = 601, learning\_rate = 0.9269, loss = square, random\_state = 42 \\ \hline GBM & n\_estimators = 400, learning\_rate = 0.05, max\_depth = 4, objective = regression\_11, random\_state = 42 \\ \hline ETR & n\_estimators = 700, min\_samples\_leaf = 5, min\_samples\_split = 20, criterion = squared\_error, random\_state = 42, n\_jobs = \(-1\) \\ \hline BR & n\_estimators = 100, max\_samples = 0.9, max\_features = 1, bootstrap = True, bootstrap\_features = False, random\_state = 42 \\ \hline SVR & kernel = poly, C = 10, epsilon = 0.01, degree = 3 \\ \hline HGBR & learning\_rate = 0.01, loss = absolute\_error, max\_depth = 7, min\_samples\_leaf = 10, random\_state = 42 \\ \hline XGBoost & n\_estimators = 1000, learning\_rate = 0.05, max\_depth = 3, gamma = 0.1, random\_state = 42, n\_jobs = \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters selected in the GA optimization process. other models.