# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 3 Results
Here, the REPTree and J48 were the best ML algorithms in terms of interpretability. Thus, the computational cost criterion served as a tiebreaker. In the interpretability ranking, the LMT and NBTree algorithms followed the REPTree and J48 algorithms, whereas the RIDOR and MLP algorithms ranked at the bottom. Computational cost, the third criterion, assesses the CPU time required for estimating the PQoS of video conferencing. Figure 4 presents the CPU time (in milliseconds) used for testing 30% of the instances in each dataset in a cloud server equipped with four Intel Haswell 2.6 GHz vCPU and 16 GB memory. As shown in Figure 4, the REPTree and RIDOR algorithms spent nearly zero milliseconds estimating PQoS in all datasets, whereas the J48 and LMT algorithms took nearly zero milliseconds to estimate PQoS only in the 2n, 5 ac, and 5 ax datasets. The MLP algorithm used less than 100 ms, and the NBTree algorithm used up to 700 ms to estimate PQoS in 30% of the instances of all datasets. In the CPU time spent for the testing phase, the REPTree was the fastest algorithm trained, with 70% of the instances of all datasets. In the list ranking CPU time used for training, the J48, RIDOR, LMT, MLP, and NBTree algorithms all placed after the REPTree algorithm, in that order. In terms of accuracy, interpretability, and computational cost, the REPTree algorithm was chosen as the best algorithm for estimating the PQoS of video conferencing services  
Figure 4: CPU time in milliseconds required for testing phase of 30% of datasets. on Wi-Fi APs. Table 3 presents the overall assessment of six ML algorithms trained for estimating the PQoS of video conferencing with respect to accuracy (A), interpretability (I), and computational cost (C) criteria. Complex algorithms such as MLP are more accurate and computationally expensive algorithms and are generally treated as a black box regarding interpretability and ease of explanation to decision makers. Therefore, there should be a trade-off between accuracy, interpretability, and computational cost. As can be seen in Table 3, the classification accuracies of all algorithms are in the same range, thus the interpretability advantages of tree-like models over RIDOR and MLP resulted in choosing J48, LMT, REPTree, and NBTree in the interpretability assessment phase. Among these, NBTree produced a computationally intensive model, which could overload the CPU of APs. Finally, among the J48, REPTree, and LMT, the REPTree was chosen as the best algorithm because it had the lowest computational cost even for the training phase. This suggests that using REPTree can impose a lower operational cost for analytics. The results demonstrated that PQoS levels for video conferencing can be estimated with a high classification accuracy (at least 92%) using only the 802.11-specific performance parameters on Wi-Fi APs in homes and buildings. This study demonstrated the proof of concept for objective assessment of the PQoS on wireless networks using ML, which can be implemented in the form of edge computing in customer premises. Although the datasets produced in this study may not represent all wireless conditions, collecting long-term data on wireless networks can improve the precision of ML estimations over time.