## 5 Model Accuracy Results  
The prediction accuracies of the black box ML and glass box ML models are evaluated based on splitting the data by a 1:4 training: testing ratio and by means of MAE (mean absolute error), RMSE (root mean square error), and R squared. The prediction accuracies of the empirical models are computed based on the Supplementary Materials in [10]. The values of the performance metrics for the measurement routes A, B, and C are presented in Tables 3-5. The code and the input data to replicate the model accuracy results can be downloaded at the Supplementary Material of this paper. Overviewing the accuracy results in Tables 3-5 by means of the obtained mean absolute errors as well as the R-squared and root mean squared error demonstrates the capability of the EBM model in terms of achieving the foremost precise predictions along the routes A and B. For route C, we attained comparable results with tree-based models, i.e., RF and XGB, which are the best predictors along route C.  
The superiority of EBM exactness compared to GAM and GNAM can be referred to as its ability to account for interactive interaction of independent variables in addition to considering their isolated effects. In addition, tree-based models (RF, XGB, and EBM) demonstrate higher precision in comparison with deep models (MLP, GNAM). Furthermore, all models show deteriorating results when it comes to route C. Negative R-squared values observed by empirical models indicate that the sum of the squares of \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & \multicolumn{2}{l}{**Empirical Models**} & \multicolumn{6}{c}{**Black Box ML Models**} & \multicolumn{3}{c}{**Glass Box ML Models**} \\ \cline{2-10}  & **Okumura–Hata** & **COST 231** & **ECC-33** & **RF** & **XGB** & **MLP** & **GAM** & **GNAM** & **EBM** \\ \hline MAE & 6.980 & 7.009 & 18.881 & 2.666 & 2.694 & 3.636 & 2.957 & 3.650 & 2.369 \\ RMSE & 10.119 & 9.994 & 20.522 & 3.402 & 3.544 & 4.773 & 3.682 & 4.745 & 2.989 \\ R Squared & \(-0.151\) & \(-0.122\) & \(-3.734\) & 0.841 & 0.82 & 0.745 & 0.848 & 0.748 & 0.900 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of prediction models along route B. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & \multicolumn{2}{l}{**Empirical Models**} & \multicolumn{6}{c}{**Black Box ML Models**} & \multicolumn{3}{c}{**Glass Box ML Models**} \\ \cline{2-10}  & **Okumura–Hata** & **COST 231** & **ECC-33** & **RF** & **XGB** & **MLP** & **GAM** & **GNAM** & **EBM** \\ \hline MAE & 8.478 & 7.073 & 13.451 & 2.541 & 2.415 & 3.554 & 2.579 & 3.447 & 2.263 \\ RMSE & 11.103 & 9.815 & 14.669 & 3.790 & 3.264 & 4.916 & 3.38 & 4.406 & 2.918 \\ R Squared & \(-0.792\) & \(-0.4\) & \(-2.128\) & 0.878 & 0.909 & 0.732 & 0.873 & 0.785 & 0.905 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of prediction models along route A. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & \multicolumn{2}{l}{**Empirical Models**} & \multicolumn{6}{c}{**Black Box ML Models**} & \multicolumn{3}{c}{**Glass Box ML Models**} \\ \cline{2-10}  & **Okumura–Hata** & **COST 231** & **ECC-33** & **RF** & **XGB** & **MLP** & **GAM** & **GNAM** & **EBM** \\ \hline MAE & 13.792 & 12.862 & 12.393 & 1.885 & 2.116 & 4.408 & 3.077 & 4.058 & 2.315 \\ RMSE & 18.559 & 17.513 & 14.976 & 2.644 & 2.893 & 6.207 & 4.129 & 5.356 & 3.031 \\ R Squared & \(-6.128\) & \(-5.347\) & \(-3.641\) & 0.868 & 0.842 & 0.273 & 0.678 & 0.459 & 0.826 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of prediction models along route C.  
the vertical distances of the points from the fitted curve (residuals) exceeds the sum of the squares of the vertical distances of the points from a horizontal line drawn at the mean PL predicted value. This can be interpreted as no suitability of empirical models to explain the PL behavior by means of the independent variables of the study.