## 3 Results and Discussions
### Dimensionality Reduction through Important Data Feature Selection Using RF
The performance of the enhanced path loss prediction obtained using the proposed method compared with the standard methods is presented, using standard metrics, such as the MAE and R. Lower MAE values obtained using the proposed hybrid RF-PS prediction across all locations are a clear indication of its optimal performance. Besides the MAE, another very relevant indicator to assess the precision accuracy of the proposed RF-PS method in comparison with measured path loss is the R. Here, R measures the close connection between the predicted and measured path losses. The closer the R-value is to 1, the stronger, healthier, and better the linear connection between the predicted and measured path losses. The linear connection is indicated using a red line in Figures 20-31. The MAE results presented in Table 2_characterize the magnitude differences between the predicted and the measured path loss_ using the proposed hybrid RF-PS prediction and two other existing methods; ordinary PS and combined RBF-PS for sites in Locations 1-4. Table 3 shows the coefficients of the developed path loss model using the proposed hybrid RF-PS approach and other key existing standard approaches. Figure 23: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 1 of location 2. Figure 24: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 2 of location 2. Figure 22: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 3 of location 1. Figure 27: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 2 of location 3. Figure 26: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 1 of location 3. Figure 25: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 3 of location 2. Figure 28: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 3 of location 3. Figure 30: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 2 of location 4. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Location** & **Site** & **RF-PS** & **PS** & **RBF-PS** \\ \hline Asaba & 1 & 2.62 & 3.71 & 3.35 \\ \cline{2-5}  & 2 & 3.39 & 4.47 & 4.45 \\ \cline{2-5}  & 3 & 3.62 & 4.79 & 4.80 \\ \hline Ontisha & 1 & 1.83 & 2.13 & 2.13 \\ \cline{2-5}  & 2 & 2.25 & 3.63 & 3.61 \\ \cline{2-5}  & 3 & 2.82 & 3.00 & 3.03 \\ \hline Awka & 1 & 1.71 & 2.82 & 2.82 \\ \cline{2-5}  & 2 & 2.53 & 4.66 & 4.40 \\ \cline{2-5}  & 3 & 3.07 & 4.02 & 4.02 \\ \hline Agbor & 1 & 3.16 & 3.68 & 3.68 \\ \cline{2-5}  & 2 & 2.83 & 4.97 & 4.97 \\ \cline{2-5}  & 3 & 3.64 & 4.24 & 4.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computed MAE values attained with developed path loss model and other key existing standard prediction approaches. Figure 31: Results showing the correlation between the measured path loss and predicted path loss using the hybrid RF-PS prediction method and two existing standard path loss prediction methods in site 3 of location 4.