## 5 Results and Discussion  
The obtained results from the 'Cross Validation' and 'Explain Prediction' operators are tabulated in Table 5, which shows the comparison between each ML model in terms of accuracy, standard deviation, classification error, the cross-validation execution time, and 'Explained Prediction' execution time when K = 10. The model with the highest accuracy is then selected for the elimination of the least important criteria for the electrical distribution substation communication technology. The output of the 'Explain Prediction' operator for the chosen ML model was specifically evaluated in terms of its supporting and contradicting prediction values. Following that, an average value for each technical criterion was calculated, and the criteria were ranked in order of importance, as shown in Table 5. Results comparisons obtained from the RapidMiner simulation. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & \begin{tabular}{l} **Cross Validation** \\ **Accuracy** \\ \end{tabular} & **Standard Deviation** & \
\begin{tabular}{l} **Execution Time** \\ **(Cross Validation)** \\ \end{tabular} &
\begin{tabular}{l} **Execution Time** \\ **(Explain Prediction)** \\ \end{tabular} \\ \hline Naive Bayes & 98.41\% & +/ \(-\) 0.79\% & 28–50 ms & 3320–3800 ms \\ \hline Decision Tree & 97.05\% & +/ \(-\) 2.94\% & 45–80 ms & 1800–2500 ms \\ \hline Random Forest & 97.95\% & +/ \(-\) 1.29\% & 980–2400 ms & 144,000–160,000 ms \\ \hline Gradient Boosted Tree & 98.07\% & +/ \(-\) 1.52\% & 3700–9400 ms & 28,000–40,000 ms \\ \hline k-NN & 97.73\% & +/ \(-\) 1.42\% & 95–150 ms & 47,000–55,000 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results comparisons obtained from the RapidMiner simulation. Figure 3: K-fold cross validation process. From Table 5, it can be observed that the Naive Bayes model showed the highest accuracy, the lowest standard deviation, and the shortest cross validation execution time. Furthermore, the 'Explain Prediction' operator execution time was the second shortest of the five models tested. As this research only required a one-time process to determine which criteria should be prioritized, accuracy took precedence over time spent. Although the random forest model's performance in cross-validation accuracy was comparable to naive Bayes, with almost a 0.5% difference and a +/ \(-\) 0.5% standard deviation, its execution times for both 'Cross Validation' and 'Explain Prediction' operators were longer. Additionally, the naive Bayes model offered several advantages over the random forest model, in which its model size was low and quite constant with respect to the data [74]. Since naive Bayes models cannot reflect complex actions, they would not overfit. On the other hand, the random forest model may result in overfitting if not carefully developed [74]. From its performance shown in Table 5, the naive Bayes ML model was selected for further analysis. Specifically, the outcomes of the 'Explain Prediction' operator for naive Bayes were used to assist in the elimination of the least important technical criteria for the electrical distribution substation communication technology. The output of the operator showed which technical criteria support or contradict the predictions. It also listed the importance of the technical criteria row by row with respect to the column, presented in numerical values. From that, an average was determined to assist in the ranking of the criteria, with the highest influence on the selection of the communication technology. Table 6 shows the average of the criteria for the 880 data points from the Naive Bayes model. It can be observed that terrain factor and interference held the most negative attributes out of the nine criteria considered. These two lowest criteria shall be omitted because they are regarded as the least important and have no significant impact on the selection of electrical distribution substation communication technologies.