# Machine-Learning-Based Path Loss Prediction
## 4 Results and Discussion
The results show that the RF and GB models demonstrated superior performance, achieving the lowest MAE and RMSE values. The RF model, with an MAE of 0.0557, an RMSE of 0.0774, and an R\({}^{2}\) of 0.97, emerged as the best-performing model, indicating its high predictive accuracy and robustness. GB also performed quite well, almost as well as RF, with an MAE of 0.0701, an RMSE of 0.0964, and an R\({}^{2}\) of 0.96. Thus, it can be concluded that GB is as effective as RF in processing complex data models in the context of this study. In contrast, the ANN and SVR models, while performing reasonably well, demonstrated higher error rates compared to RF and GB. The AB and LR were among the worst-performing machine learning models with the lowest R\({}^{2}\) and the highest RMSE, respectively. On the other hand, the log-distance model was the best-performing empirical model with 3.9884 RMSE and 0.92 R\({}^{2}\), but it generally lagged behind machine learning models. The two-ray and log-ray models also performed poorly, with RMSE values of 10.038 and 8.4032 and R\({}^{2}\) values of 0.32 and 0.29, respectively. This clearly shows that, whereas machine-learning models have the capability of highly accurate predictions for various complex V2V communication scenarios, the limitations of empirical approaches negatively affect the prediction performance. Overall, these results suggest the necessity of adopting advanced machine learning techniques for more accurate and reliable path loss predictions in V2V communication. Figure 6: Visual performance comparison of machine learning models for path loss prediction. Then, the Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and F-Test methods were used as preprocessing steps before rerunning the algorithms to optimize the performance of the machine learning models. PCA was employed to decrease the number of dimensions in the data while preserving a significant portion of the variability. This process aims to simplify the model and improve computational performance. The F-Test facilitated the selection of important features, thus guaranteeing that only the most useful factors were considered, thereby boosting the accuracy of the model. The MRMR method was used to identify features that optimize their relevance to the target parameter and minimize duplication between them. Table 6 shows the feature selection weights of various features using PCA, MRMR, and F-Test methods. Here, PCA highlights four out of nine features, MRMR highlights eight features, and F-Test highlights three features, of which distance and weather (sunny or snowy) are the common features weighted in each. Subsequently, to investigate the individual effects of each method on model adaptability and predictive performance, the error outputs were calculated when each method was used separately and when it was not, as shown in Table 7. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Features** & **PCA (\%)** & **MRMR** & **F-Test** \\ \hline Distance & 43.1 & 1.7983 & \(\infty\) \\ Snowy & 26.7 & 0.8683 & \(\infty\) \\ Dual Antenna & - & 0.8683 & - \\ Sunny & 13.9 & 0.8683 & \(\infty\) \\ Same Direction & 12.3 & 0.5973 & 736.6049 \\ Opposite Direction & - & - & 369.3322 \\ Antenna Outside & - & 0.5295 & - \\ Antenna Inside & - & 0.5295 & - \\ Single Antenna & - & 0.6326 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Obtained feature weights of the PCA, MRMR and F-Test Methods.