## 4 Results and Discussion  
### Predictive ML Models Created over a Set of Predictors Optimized by the RReliefF Algorithm  
In order to compare performance, the optimization results of the set of independent variables by the RReliefF algorithm for different values of k-Nearest Neighbors are shown in Table 3. The values of this parameter are chosen empirically. For \(k=10\), \(k=15\) and \(k=20\), the algorithm ranked the independent variables by weighting coefficients in the same ranking. The most influential predictor for all three cases is DL.16 QAM.TB.Retrans, while Average_DL_User_Throughput is in last place with a negative weighting coefficient for each \(k\). According to expression (8), with the conventional value \(\alpha=0.05\) and the default value m = 31,143, the \(RT\) value is selected in the interval \(0<RT<0.025\). However, in practice, instead of a certain value of \(RT\), and in accordance with the limitations, a few of the most important predictors that affect the prediction of the dependent variable are often chosen. Considering that the number of variables with weighting coefficients greater than 0 is equal to 12 in the observed case, the first six ranked predictors, according to Table 3, are selected as the final number of inputs. The \(RT\) threshold value that can be set hypothetically, and  
Figure 8: ML models ranked by accuracy and interpretability in various published research papers [42; 43; 44; 46]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**Independent Variable or Predictor**} & \multicolumn{3}{c}{**Predictor Weighting Coefficients for Individual Values of \(k\)**} \\ \cline{3-5}  & & \(k=10\) & \(k=15\) & \(k=20\) \\ \hline
1 & DL\_16 QAM.TB.Retrans & 0.0061 & 0.0065 & 0.007 \\
2 & DL\_QPSK.TB.Retrans & 0.006 & 0.0064 & 0.0067 \\
3 & Cell\_Traffic Volume, UL & 0.0041 & 0.0044 & 0.0045 \\
4 & DL\_PRB\_Usage\_Rate & 0.0037 & 0.004 & 0.0043 \\
5 & Cell\_Traffic Volume\_DL & 0.0033 & 0.0035 & 0.0038 \\
6 & UL\_Average\_Interference & 0.0028 & 0.0031 & 0.0033 \\
7 & DL\_6 QAM.TB.Retrans & 0.0027 & 0.0028 & 0.0029 \\
8 & Cell & 0.0024 & 0.0025 & 0.0027 \\
9 & UL\_IBLER & 0.001 & 0.001 & 0.0012 \\
10 & UL\_ReTrans\_Rate & 0.0009 & 0.001 & 0.0011 \\
11 & Cell\_Uplink\_Average\_Throughput & 0.0006 & 0.0006 & 0.0007 \\
12 & Average\_UL\_User\_Throughput & 0.0001 & 0.0001 & 0.0001 \\
13 & Average\_CQI & \(-0.0008\) & \(-0.0008\) & \(-0.0009\) \\
14 & DL\_ReTrans\_Rate & \(-0.0013\) & \(-0.0013\) & \(-0.0014\) \\
15 & DL\_IBLER & \(-0.0015\) & \(-0.0016\) & \(-0.0017\) \\
16 & Cell\_Downlink\_Average\_Throughput & \(-0.0019\) & \(-0.002\) & \(-0.0021\) \\
17 & Average\_DL\_User\_Throughput & \(-0.0027\) & \(-0.0029\) & \(-0.003\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimization results of the set of independent variables by the RReliefF algorithm. which can correspond to this selection of the optimal set of variables, is \(RT\) = 0.0028. This threshold applies to all three values of \(k\). Table 4 shows the ranked \(RE\) values and correlations for the three tested models that were created over the data set optimized by the RReliefF algorithm. Pearson's correlation coefficients \(r\) are calculated as follows:  
\[r=\frac{\sum(D_{EtOei}-D_{EtOeAVG})(D_{PREDi}-D_{PREDAVG})}{\sqrt{\sum(D_{EtOei} -D_{EtOeAVG})^{2}\sum(D_{PREDi}-D_{PREDAVG})^{2}}} \tag{10}\]  
where \(D_{PREDAVG}\) is the arithmetic mean of the variable \(D_{PREDI}\). According to Table 4, the best predictive performance is shown by the model based on k-NN, which has \(RE\) = 0.109 and the correlation coefficient equal to 0.944. That is why this model is selected as the best solution in the approach to predictor set optimization with the RReliefF algorithm. The SVM model has the highest relative error, which is \(RE\) = 0.205, but also the lowest correlation value, which is equal to 0.893.