## 6 Conclusions  
In summary, in this work, a service classification analysis was conducted in 5G/B5G networks using a dataset containing KPI and KQI variables. Principal component analysis (PCA) was applied to effectively reduce the dimensionality of the dataset while employing the same ML classification algorithms (DT, RF, SVM, KNN, MLPC) as in [1]. The results indicate that the MLPC algorithm achieved an accuracy of 90.9% and a [PERSON] correlation coefficient of 89.5%. While these results are relevant and satisfactory, they did not surpass the performance metrics obtained in prior research.  
As demonstrated in Figure 4, the initial application of these algorithms revealed sub-optimal learning outcomes. In response, we undertook an exploration of hyperparameters for the ML algorithms. Following that, the hyperparameters were fine-tuned and subsequently implemented, resulting in notable enhancements in performance. Notably, the SVM algorithm exhibited a precision of 98.1% and an F1 score of 98%. Figure 8 directly compares the SVM result and the earlier findings.  
In conclusion, this study underscores the critical role of PCA analysis and hyperparameter optimization in service classification within 5G/B5G networks. It is important to note that a more precise service classifier directly translates into improved quality of service (QoS), ensuring that users experience higher performance and reliability standards, and thereby meeting and surpassing the expectations outlined in service-level agreements (SLA). These insights are poised to significantly enhance the QoS and ensure SLA compliance in next-generation network environments.  
The main limitation of this research is the lack of a public 5G dataset that contains real measurements of QoS parameters from service providers. Service providers do not often share their networks' operational data, making it challenging to find operational data, which is why synthetic datasets are used. In future work, an area of interest will be the ability to execute ML algorithms to classify services according to performance and quality parameters extracted from operators.  
In future work, we will consider the implementation of dynamic PCA, an adaptive approach that can dynamically adjust PCA parameters to suit evolving network conditions.  
Conceptualization, J.E.P.-V., J.E.L.-R. and J.D.G.-F.; Methodology, J.D.G.-F., J.E.P.-V. and J.T.-R.; Software, J.D.G.-F.; Validation, R.R.-R., J.T.-R. and M.A.A.-A.; data curation, J.D.G.-F. and J.E.P.-V.; writing--original draft preparation, J.D.G.-F., J.E.P.-V. and J.E.L.-R.; writing--review and editing, J.E.L.-R., R.R.-R., M.A.A.-A. and J.T.-R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, Baja California (CICESE), Mexico, and the Consejo Nacional de Humanidades, Ciencias y Tecnologias (CONAHCYT), Mexico.  
Data Availability Statement: The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
The authors would like to thank to the CICESE and the CONAHCYT for their support.