# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 3 Results
As Table 2 shows, the REPTree models performed quite well in predicting bad and good quality classes (MOS = 1 and MOS = 4, respectively), whereas they underperformed in predicting poor and fair quality classes. Since few instances were labeled with MOS = 2 and MOS = 3 in the datasets, all six candidate algorithms achieved low levels of precision and recall in predicting these two classes. Furthermore, 2 ax and 5 ax datasets were each missing up to 30% of their values, which likely hindered ML algorithms' attempts to achieve higher accuracy in predicting MOS classes (see Table 1). The confusion matrix can provide additional metrics, including precision, recall, and F1 score, to evaluate the performance of ML algorithms. The precision measures the proportion of correct predictions and recall measures the proportion of correct prediction of true positives. In order to simplify the performance evaluation, F1 score presents harmonic mean of precision and recall, which is suitable for evaluating the performance of algorithms trained with an imbalanced dataset. Figure 3 presents the F1 score of candidate algorithms tested with 30% of each dataset. These results indicate that candidate algorithms classified the perceived quality of video conferencing precisely, mostly because of the performance parameters used to train algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**2n Classified as**} & \multicolumn{4}{c}{**2 ax Classified as**} \\ \hline  & **a** & **b** & **c** & **d** & & **a** & **b** & **c** & **d** \\ \hline a = 1 & 233 & 3 & 7 & 43 & a = 1 & 982 & 2 & 2 & 163 \\ b = 2 & 6 & 9 & 0 & 16 & b = 2 & 8 & 8 & 0 & 11 \\ c = 3 & 6 & 5 & 9 & 42 & c = 3 & 9 & 0 & 12 & 22 \\ d = 4 & 22 & 3 & 8 & 2607 & d = 4 & 23 & 0 & 2 & 2457 \\ \hline \hline  & \multicolumn{4}{c}{**5 ac Classified as**} & \multicolumn{4}{c}{**5 ax Classified as**} \\ \hline  & **a** & **b** & **c** & **d** & & **a** & **b** & **c** & **d** \\ \hline a = 1 & 1074 & 0 & 0 & 25 & a = 1 & 705 & 0 & 0 & 78 \\ b = 2 & 0 & 7 & 0 & 6 & b = 2 & 3 & 0 & 0 & 5 \\ c = 3 & 1 & 1 & 9 & 7 & c = 3 & 1 & 0 & 1 & 13 \\ d = 4 & 21 & 1 & 0 & 1409 & d = 4 & 48 & 0 & 1 & 2113 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of REPTree models produced for 2n, 2 ax, 5 ac and 5 ax datasets. Figure 3: The F1 score of candidate ML algorithms testing 30% of datasets. Regardless of missing values, feature engineering techniques such as replacing missing values with modes and means from training data can slightly enhance the classification accuracy, and in particular can enhance the poor and fair classes prediction. Since the classification accuracies of all six candidate algorithms are quite similar, the interpretability criterion facilitates efficiency assessment by choosing the best model to interpret and identify the root causes of quality degradation in wireless networks. Therefore, models that are easy to interpret and easily traceable are particularly valuable in estimating the PQoS of video conferencing. The tree-like ML models are preferred because a human observer can explain a tree model by deconstructing each decision path into individual steps. A tree model generally is a simple model to visualize, and the tree presentation can depict interactions between features in the dataset. Furthermore, a tree structure provides contrastive presentation of decision paths such that a human observer can always compare the possible decision paths with each other. In contrast to other complex models, observers can perceive the order of feature importance from tree visualization. Since ML algorithms that produce tree-like models are easy to interpret, the J48, REPTree, LMT, and NBTree fulfill the interpretability criterion. In addition, the LMT and NBTree produced a combination of tree-model with logistic regression and naive Bayes functions, respectively, ultimately achieving superior results in some use cases, compared with pure tree models.