## 3 Methodology  
In this section, we present the fundamental process covered by this study to evaluate the effectiveness of service classification using SL algorithms in the context of 5G/B5G networks. Our approach considers three crucial phases that converge in the search for an optimal solution: the first involves the reduction of the dimensionality of our data using PCA, followed by the application of SL algorithms, and culminating in the search for hyperparameters that allow us to obtain more solid and precise results. Figure 1 shows the block diagram of the processes followed in this work. This diagram provides an overview of the interconnected stages that make up our methodological approach. This section thoroughly explores each phase, highlighting the techniques used in each step. Figure 1: Block diagram of our proposal. 5G Dataset is from reference [1]. This comprehensive approach allows for a deeper understanding of our case study; it demonstrates the importance of each phase in obtaining accurate and relevant results in the context of next-generation networks. This work evaluates the SL scheme's performance in classifying services after passing the training data through a PCA procedure. We used the same dataset and SL algorithms (DT, RF, SVM, KNN, and MLPC) for this analysis as in [1]. This research focuses on precisely evaluating the PCA's impact on the data. The dataset used in this study was created manually following communication standards. The dataset includes 165 samples and 13 variables, which are key performance indicators (KPIs) and key quality indicators (KQIs) of 5G services. Variables represent essential performance metrics such as latency, throughput, and packet loss, among others. We performed the PCA analysis to reduce the dimension of the dataset, as shown in Figure 1. We had to normalize the features of the 5G dataset because we needed each feature to contribute equally. We calculated the accumulative variance and observed how principal components retained 95% of the original variance. Derived from this process, we created a PCA 5G dataset and a group of supervised learning models to classify the new data (PCA dataset). We performed a cross-validation evaluation and compared the performance metrics of the models obtained in this study with the previous research results. Since we want to measure the impact of PCA on the dataset to guarantee the comparability of the results, we use the same parameters to fit the machine learning models in both studies. In addition, we performed a detailed comparison of the results obtained from the confusion matrices and performance metrics. Later, we search for the best hyperparameters for each algorithm to obtain the best possible results in the performance metrics. We must observe the confusion matrix to evaluate the performance metrics in an ML problem. A confusion matrix visualizes the predictive model's performance, presents confusion in two labels, and permits us to obtain the performance metrics' equations. Each row represents the current label for the test values (Y test), and the matrix columns represent the number of predictions for each label (Y) made by the predictive model. Table 2 shows an example of a confusion matrix. * True Positives: the number of current values classified as belonging to a particular class, for which the model's prediction is correct;
* False Positives: these are the current values classified as belonging to an incorrect class (they are considered by the model to be positive, but the prediction is wrong);
* False Negatives: these are values that belong to a particular class but are classified differently (incorrect prediction);
* True Negatives: these are observations that do not belong to a given class and are classified correctly. A series of metrics can be derived from the results in the confusion matrix of Table 2 and used to evaluate the performance of the predictive model. The metrics are accuracy, precision, recall, and F1 score [14].