## 1 Introduction  
Nowadays, Internet speed is an eminent concern for both users and service providers; hence, cellular companies are in a battle for fifth generation (5G) Internet implementation to provide a super-fast and reliable connection to their users [1]. So, enhancing the reliability, capacity, and speed of channels is mandatory. There are actions to accomplish those tasks, such as massive multiple-input and multiple-output (MIMO) or low power and low latency for the tactile internet, among others [2]. The primary focus of 5G networks is to enhance the transmission speed, capacity, and reliability of wireless channels. The fulfillment of these tasks is possible with the use of low power and low latency for the tactile internet, the Internet of Things (IoT), massive multiple-input and multiple-output (MIMO), robotics, autonomous vehicles, and industry [2]. It is essential for real-time applications and seamless connectivity with low latency requirements to have a reliable network, and 5G offers a latency of 1 ms to satisfy this requirement [3]. In the future, the above traits of 5G will assist in upcoming big data applications such as diagnosing critical-life situations in hospitals, fast money transfer in a stealthy way, handling inventory in warehouses, and much more [4]. LTE, 4G, and their earlier generations cannot support the latest traffic-intensive applications with quality of service (QoS). Here is where the 5G characteristic offers an average data rate of more than 100 megabits per second (Mbps) and data rates of up to 20 Gbps (faster than 4G) to meet these requirements [5]. Applying supervised learning algorithms to high-dimensional datasets is a common challenge in various areas of research and practical application. One of the most widely used methods that allow for the extraction of the most relevant characteristics of the data to reduce its dimensionality and represent them in a lower dimensional space [6; 7] is principal component analysis (PCA). However, although PCA can improve the performance of supervised learning algorithms, it can also hide important information and affect the performance of these algorithms. This article presents a study on applying supervised learning algorithms to a dataset after having undergone a PCA procedure. The main objective is to analyze how PCA affects the performance of supervised learning algorithms for classifying 5G/B5G services in terms of processing time and generalization capacity. We also analyze if PCA is suitable based on the following decision metrics: accuracy, precision, recall, f1 score, and Matthew's correlation coefficient (MCC). The methodology used in this article considers the use of a public dataset and the application of the five supervised learning algorithms used by the authors in [1], such as decision tree (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (KNN), and multi-layer perceptron classifier (MLPC). In addition, we are applying a K-Folds cross-validation (K = 10) procedure to ensure the validity of the results. The main contributions of this study are as follows:  
* The reduction of dimension in QoS parameters with respect to the 5G dataset, maintaining 95% of data variance;
* The evaluation of the impact of PCA on the performance of supervised learning algorithms and the comparison of the results obtained with a previous study that did not use PCA;
* The identification of the best hyperparameters of five SL algorithms to obtain the best possible results for the PCA 5G dataset;
* The improvement of the performance metrics of SL models concerning previous studies, with an accuracy of 98.1% and an F1 score of 98%. These results can benefit various application areas of network management and telecommunications service providers. The organization of the rest of this paper is as follows: Section 2 discusses the PCA involved in service classification and the methodology applied. Section 3 describes the methodology used in the present work and how we used PCA to reduce the characteristics of the database.