## 2 Methodology
### Estimation of Frequency-Selective Signal Field Strength
The differentiation between decision trees depends largely on the maximum number of features used during the development process. The optimal values for these parameters can be determined using ten-fold cross-validation [25; 36; 37]. In this method, the dataset is randomly divided into ten subsets, with each subset used once as the validation set, and the validation process is repeated ten times, in line with the ten-fold cross-validation approach. The entire model training process was conducted using an 8 th Generation Intel Core i7 processor, 16 GB of RAM, a 64-bit operating system, and a 1 TB hard drive, with Python as the development software and Scikit-learn 0.24.1 as the framework. The model took approximately 3821.6 s, or 1.06 h, to complete training. To assess the real performance of the machine learning model with various parameters, we averaged the MAPE and RMSE across ten validation sets. Through this evaluation, we determined the values of \(D\), \(\mathbb{N}_{1}\), and \(\mathbb{N}_{2}\) to be 12, 18, and 3, respectively. Specific parameters and settings for the random forest regression model are provided in Table 7.  
Using the random forest regression model with the optimal settings mentioned above, Figure 7 presents a comparison between the estimated electric field strength and the measured values, with index numbers ranging from 1 to 404 in the test set. According to Figure 8, the predicted values appear to be consistently lower than the measured ones in regions where the field strength is high. For instance, Figure 7 shows that estimation errors can reach around 5 V/m. Such discrepancies could mean the difference between a measurement point complying with safety limits or not. This highlights the importance of conducting on-site measurements to ensure accurate compliance verification. Figure 8 compares the measured values with the predicted values in the CDF plots of electromagnetic fields. The results indicate a strong agreement between the estimated and measured electric field intensities at different locations. Any observed discrepancies are likely due to occasional interference in the measurement environment, such as the presence of other 5G terminals or urban railroads, which were not considered in the estimation process. Additionally, we calculated the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) to evaluate the performance of the estimation method. The results revealed an RMSE of 0.5928 and an MAPE of 5.985%. These findings further validate the effectiveness and reliability of the estimation technique. The proposed model is capable of accurately estimating the radiation field intensity of 5G base stations by considering a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Value** \\ \hline \(\mathbb{N}_{1}\) & Number of decision tree & 18 \\ \hline \(\mathbb{N}_{2}\) & Maximum number of features allowed to be & \\  & employed in decision tree design & 3 \\ \hline D & Maximum depth of decision trees & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Setups and parameters for the random forest regression model. broad range of factors that influence electromagnetic radiation, thanks to its comprehensive analysis of multi-dimensional data.