## 5 Results and Discussion
### Multilayer Perceptron Model (MLP)
The arrangement of the residuals for the two output variables is shown in Figure 8. The model is of better quality and more accurate if it has residuals grouped around the horizontal zero line, which, in this case, is visually clearly visible. \begin{table}
\begin{tabular}{c c} \hline \hline
**Determination Coefficient** & **Qualitative Characteristic** \\ \hline
0.1–0.3 & Weak \\
0.3–0.5 & Moderate \\
0.5–0.7 & Salient \\
0.7–0.9 & High \\
0.9–0.99 & Very high \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Chaddock scale. Figure 8: Residual distribution graph for two output variables in MLP prediction model. Figure 7: Scatter plots of the model testing results. One of the graphical results, generated by IBM SPSS technology, which represents the ranking of independent variables according to the _importance of influencing_ the variability of the dependent variable in the model is shown in Figure 9. The measure showing how much the value created by the prediction changes for different values of the independent variable represents the _importance_ of the independent variable expressed numerically at the lower position of the abscissa of the graph, while the _normalized importance_, obtained by dividing the importance with the largest individual value, is expressed as a percentage and presented at the upper position of the abscissa of the graph in Figure 9.  
The interpretability of the model, as its ability to present the results to the researcher in an understandable degree, can be seen in Figure 9 and indicates that the variable _DL Cell Traffic Volume_ has the greatest _importance_ of influencing the changes in the values of the dependent variables. This conclusion is logical and expected considering that the throughput in the network, by definition, directly depends on the total realized traffic or the amount of data transmitted during the observed time. In contrast, the _Average CQI_ variable is at the bottom of the rankings and has the least importance of influencing the changes in the value of the dependent variable. This is explained by the fact that MLP well models the nonlinear dependences of output on input. The observed variable is linearly related to the corresponding modulation and coding scheme of communication, and it is important for the size of the transport block when transmitting data through a channel which directly influences the throughput of specific classes of traffic. Based on the fact that the creation of the model in this paper requires previously collected research data related to the network traffic and its parameters, it is concluded that, in this case a statistical approach is applied to the traffic modeling, which is otherwise based on _traces_ (recording of combined traffic). Another possible approach is through an _emulator_, and, in some cases, a _hybrid_ approach is possible. Since the variable DL Cell Traffic Volume means that it is the total traffic in the DL direction at the cell level, the created predictive model is a _model of combined traffic_. When models are created for each service, i.e.,  
Figure 9: Graphical overview of the results of the influence of the importance of individual independent variables in the MLP prediction model. for each of the nine traffic classes shown in Table 5, this means that an approach based on _models of information sources_ is applied, which are characterized by greater accuracy, but also greater complexity than the previous one. According to [22], the models of combined traffic are, in most cases, more suitable for application in the prediction of traffic in networks, and in [23] a good overview of the traffic models is presented. The analysis of possible differences in the results of training and testing of different variants of the model in the three iterations shown was carried out by appropriate statistical tests. If the three measurements of the RE values are considered as three statistical groups, each tested variant of the MLP model represents one experimental unit over which the experiment is repeated three times.