## 5 Results and Discussion
### Multilayer Perceptron Model (MLP)
In the initial state, when the model is not yet trained, the value of the sum of the square error is significantly higher and is calculated by using the values of the arithmetic mean of that dependent variable as the prediction values of the observed dependent variable for all measurements. According to Table 9, the error function after model training has a value of 4882.801 and when testing the model, its value is smaller and amounts to 2089.438. Two criteria for stopping the training process were set: (1) expiration of a time period of 15 min and (2) reaching the number of five consecutive iterations without reducing the error function. By meeting at least one of the above criteria, the training process is interrupted, and in the specific case of the selected MLP model, condition 2 is met, as can be seen in Table 9.  
Figure 7 graphically shows the best individual result from the set of 30 variants of the MLP model testing, in the form of _scatter plots_, i.e., prediction results for the two observed dependent variables. The abscissa shows the values from the test set, while the ordinate indicates the prediction values. Based on the obtained coordinates of the points and their arrangement, it is possible to visually evaluate the accuracy of the model. This way of presenting the prediction results is suitable for determining the coefficient of determination (R\({}^{2}\)) which describes the percentage of variance of the target variable, which is explained by the model. For the dependent variable _Cell Downlink Average Throughput_, the best individual result of training and testing gives the value of the coefficient of determination R\({}^{2}\) = 0.899 while for the _Average User Downlink Throughput_, this value is R\({}^{2}\) = 0.885. The coefficient of determination is higher if the points are grouped around a line defined by the equation \(y=x\), which is confirmed in Figure 7. If the R\({}^{2}\) values obtained in this way are compared with those calculated for the linear models, it can be concluded that MLP has a significantly higher accuracy of performance prediction or model quality because \begin{table}
\begin{tabular}{c c c} \hline \hline  & Sum of squares error & 4882.801 \\ \hline \multirow{4}{*}{Training} & Average overall relative error & 0.108 \\ \cline{2-3}  & & Cell downlink average throughput & 0.102 \\ \cline{2-3}  & & Average user downlink throughput & 0.114 \\ \cline{2-3}  & & Stopping rule used & Five consecutive step(s) with no decrease in error \\ \cline{2-3}  & & Training time & 0:01:00 \\ \hline \multirow{4}{*}{Testing} & Sum of squares error & 2089.438 \\  & Average overall relative error & 0.106 \\ \cline{1-1}  & Relative error for scale dependents & Cell downlink average throughput & 0.098 \\ \cline{1-1}  & & Average user downlink throughput & 0.115 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The most important results of training and testing of the selected MLP model. for cell downlink average throughput: 0.899 > 0.541 and  
for average user downlink throughput: 0.885 > 0.543. The _Chaddock_ scale, presented in Table 10[51], was used to qualitatively evaluate the coefficient of determination R\({}^{2}\). It can be seen that the accuracy performance of the MLP model is in the range of 0.7-0.9 and qualifies as high. On the other hand, the accuracy of prediction of linear models based on the coefficients of determination can be assessed as _salient_. Comparing the prediction accuracy of the MLP model with the linear model, it is obvious that it has higher, i.e., high prediction accuracy, but it is also characterized by incomparably higher complexity compared to the linear model. As a quality parameter of the created predictive model, used _Residual_ (\(r\)) is used which represents the difference between the real value (\(RV\)) and the prediction value (\(PV\)) of the observed variable: \(r=RV-PV\).