# A REM Update Methodology Based on Clustering and Random Forest
## 4 Evaluation
### REM Update Evaluation
For instance, in the case of RF with 4 clusters and a 100 \(\times\) 100 grid, a total of 10,000 samples were assigned to their respective clusters, and the corresponding RF model predicted the RSSI value for each sample, resulting in a total time of 0.112 s. Moreover, we see that the proposed RF algorithm \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**MAPE** & \multicolumn{3}{c}{**4 Clusters**} & \multicolumn{3}{c}{**Without Clustering**} \\ \hline
**Model for Prediction** & \(\mathbf{t}_{1}\) & \(\mathbf{t}_{2}\) & \(\mathbf{t}_{3}\) & \(\mathbf{t}_{1}\) & \(\mathbf{t}_{2}\) & \(\mathbf{t}_{3}\) \\ \hline RF & 6.10\% & 5.03\% & 1.92\% & 10.52\% & 6.60\% & 12.40\% \\ SVR & 7.90\% & 6.01\% & 4.90\% & 15.89\% & 8.74\% & 15.54\% \\ MLP & 12.98\% & 5.76\% & 4.55\% & 27.27\% & 18.56\% & 7.97\% \\ AdaBoost & 6.40\% & 5.49\% & 3.82\% & 13.37\% & 6.78\% & 9.03\% \\ \hline
**RMSE** & \multicolumn{3}{c}{**4 Clusters**} & \multicolumn{3}{c}{**Without Clustering**} \\ \hline
**Model for Prediction** & \(\mathbf{t}_{1}\) & \(\mathbf{t}_{2}\) & \(\mathbf{t}_{3}\) & \(\mathbf{t}_{1}\) & \(\mathbf{t}_{2}\) & \(\mathbf{t}_{3}\) \\ \hline RF & 3.731 & 3.401 & 2.069 & 6.100 & 4.019 & 8.167 \\ SVR & 4.952 & 3.823 & 3.439 & 8.996 & 5.174 & 9.514 \\ MLP & 9.959 & 3.538 & 3.018 & 15.811 & 11.235 & 5.193 \\ AdaBoost & 3.952 & 3.422 & 2.527 & 7.513 & 3.960 & 6.064 \\ \hline
**R2 Score** & \multicolumn{3}{c}{**4 Clusters**} & \multicolumn{3}{c}{**Without Clustering**} \\ \hline
**Model for prediction** & \(\mathbf{t}_{1}\) & \(\mathbf{t}_{2}\) & \(\mathbf{t}_{3}\) & \(\mathbf{t}_{1}\) & \(\mathbf{t}_{2}\) & \(\mathbf{t}_{3}\) \\ \hline RF & 0.646 & 0.735 & 0.842 & 0.055 & 0.630 & \(-1.463\) \\ SVR & 0.563 & 0.598 & 0.440 & \(-1.056\) & 0.387 & \(-2.342\) \\ MLP & \(-1.520\) & 0.714 & 0.664 & \(-5.351\) & -1.888 & 0.004 \\ AdaBoost & 0.603 & 0.732 & 0.764 & \(-0.434\) & 0.641 & \(-0.358\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error evaluations from newly collected measurements. achieved the second lowest computational time among the compared ML methods with clustering. Note that MLP has the lowest computational time for the grid prediction with clustering but also has the highest error in the prediction as presented in Table 2.  
Next, we consider a second scenario by moving the location of the AP. In particular, the AP is relocated to the other corner of the room, which significantly changes the coverage condition in the area of interest. Figure 9 illustrates the REM that was obtained with RF under ideal conditions when data measurements for the entire room were collected after the relocation of the AP. The colormap limits for the current axes have been adjusted for better data visualization. By comparing Figures 4a and 9, we can observe the significant changes in the wireless conditions of the area of interest and the importance of timely REM updates. Similar to the previously considered scenario, new data were acquired at different times as illustrated in Figure 10. Then, the proposed update methodology was performed based on the three datasets collected at times \(\mathbf{t}_{1}\), \(\mathbf{t}_{2}\) and \(\mathbf{t}_{3}\). Figure 11 shows the results of the proposed update methodology for the three sets of newly collected data. In Figure 11a,