## 5 Results Discussion  
In this section, we delve deeper into the outcomes of our study regarding service classification in 5G/B5G networks after applying PCA and hyperparameters. We present the confusion matrices and the performance metrics. Moreover, we conduct a direct comparison with the results of a previous study. This analysis phase is pivotal as it allows us to comprehend our approach's effectiveness and relevance in the context of next-generation networks. A distinctive aspect of our research is the direct comparison with a prior study that addressed service classification in 5G/B5G networks without applying PCA. This comparison offers a clear insight into how dimensionality reduction through PCA influences the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SL Algorithms**} & \multirow{2}{*}{**Hyperparameters**} & \multirow{2}{*}{**Best Value**} & \multicolumn{2}{c}{**K-Folds (K = 10)**} \\  & & & \multicolumn{2}{c}{**Cross-Validation Results**} \\ \hline \multirow{3}{*}{DT} & criterion & gini & \multirow{3}{*}{82.58} \\  & max\_depth & 6 & & \\  & min\_samples\_split & 2 & \\ \hline \multirow{3}{*}{RF} & criterion & gini & \multirow{3}{*}{87.25} \\  & max\_depth & 5 & \\  & min\_samples\_split & 2 & \\  & n\_estimators & 100 & \\ \hline \multirow{3}{*}{SVM} & kernel & linear & \multirow{3}{*}{96.92} \\  & C parameter & \(10^{3}\) & \\ \hline \multirow{3}{*}{KNN} & n\_neighbors & 3 & \multirow{3}{*}{85.55} \\  & weights & distance & \\  & algorithm & auto & \\ \cline{1-1}  & p & 2 & \\ \hline \multirow{3}{*}{MLPC} & hidden\_layer\_sizes & (50, 100, 50) & \multirow{3}{*}{93.90} \\  & activation & tanh & \\ \cline{1-1}  & alpha & 0.0001 & \\ \cline{1-1}  & solver & adam & \\ \cline{1-1}  & max\_iter & 3000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Best hyperparameters of the algorithms. performance of SL algorithms. The outcomes of this comparison are pivotal to determining the effectiveness of our approach and its impact on the current state of research in this field. Figure 6 shows the confusion matrices obtained from these models. Now, we applied Equations (1)-(5) of the metrics obtained from the confusion matrix to evaluate the performance of the predictive models with the hyperparameters. Table 7 shows the results. Figure 7 shows the learning curve of the SVM algorithm with hyperparameters. So, we can see now that the SL algorithm was learning well with the validation set of data (X test and Y test). We obtained better results than those shown in previous research work. Figure 8 shows a comparative table between the SVM model, which obtained the best results in this work, and the RF from previous studies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SL** & \multirow{2}{*}{**Accuracy (\%)**} & **Precision** & **Recall Macro** & **F1 Score** & \multirow{2}{*}{**MCC (\%)**} \\
**Algorithms** & & & & **Macro (\%)** & **(\%)** & **Macro (\%)** & \\ \hline DT & 87.9 & 88.0 & 87.9 & 86.9 & 86.3 \\ RF & 84.8 & 89.2 & 86.3 & 86.1 & 82.9 \\ SVM & 97.0 & 98.1 & 98.1 & 98.0 & 96.6 \\ KNN & 90.9 & 93.7 & 92.2 & 91.9 & 89.8 \\ MLPC & 90.9 & 95.4 & 90.7 & 92.0 & 89.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model metric results for the simulation with hyperparameters. Figure 6: Confusion matrices of each model with hyperparameters (DT, RF, SVM, KNN, and MLPC). Our research results can benefit various application areas of network management and telecommunications service providers. The areas of applicability of this work are as follows:  
1. Improvement of QoS in new generation networks: Through dimensionality reduction using PCA and the search for hyperparameters, it allows for a more precise and efficient classification of services. Our approach directly impacts the end-user experience by ensuring reliability and efficiency in the delivery of network services. Users of 5G/B5G services will experience more robust connectivity and greater satisfaction due to improved QoS. Figure 8: Comparison of the results obtained in both papers.