## 4 Result and Discussion
### Confusion Matrix Performance for Decision Tree on Different Predictor Selection  
The confusion matrix presents the performance of the decision tree based on different predictor selection and different testing samples. Figure 17 illustrates the forms of confusion matrix for the decision tree method. In this study, one versus one (OVO) error correcting output codes (ECOC) have been trained, and ensemble Bag parameters as binary learner are implemented to outperform multiclass output. Accuracy, sensitivity, specificity, precision, and F\({}_{1}\) scores are extracted from the confusion matrix as shown in Table 15. \begin{table}
\begin{tabular}{c c c c c} \hline
**Train/Test** & **Lightning** & **Insulator** & **Tree** & **Crane** \\ \hline Lightning & 100\% (55/55) & - & - & - \\ Insulator & - & 100\% (37/37) & - & - \\ Tree & - & - & 100\% (32/32) & - \\ Crane & - & - & 3 & 93.2\% (41/44) \\ \hline \end{tabular}
\end{table} 
Table 13: Accuracy of the created decision tree w.r.t. interaction-curvature predictor selection on test set data for set data for 80% training and 20% testing. \begin{table}
\begin{tabular}{c c c c c} \hline
**Train/Test** & **Lightning** & **Insulator** & **Tree** & **Crane** \\ \hline Lightning & 100\% (38/38) & - & - & - \\ Insulator & - & 100\% (44/44) & - & - \\ Tree & - & - & 100\% (48/48) & - \\ Crane & - & - & 1 & 97.4\% (37/38) \\ \hline \end{tabular}
\end{table} 
Table 11: Accuracy of the created decision tree w.r.t. all splits predictor selection on test set data for 80% training and 20% testing. \begin{table}
\begin{tabular}{c c c c c} \hline
**Train/Test** & **Lightning** & **Insulator** & **Tree** & **Crane** \\ \hline Lightning & 100\% (38/38) & - & - & - \\ Insulator & - & 100\% (44/44) & - & - \\ Tree & - & - & 100\% (48/48) & - \\ Crane & - & - & 1 & 97.4\% (37/38) \\ \hline \end{tabular}
\end{table} 
Table 12: Accuracy of the created decision tree w.r.t. curvature predictor selection on test set data for 80% training and 20% testing. \begin{table}
\begin{tabular}{c c c c} \hline
**Train/Test** & **Lightning** & **Insulator** & **Tree** & **Crane** \\ \hline Lightning & 100\% (42/42) & - & - & - \\ Insulator & - & 100\% (50/50) & - & - \\ Tree & - & - & 100\% (35/35) & - \\ Crane & - & - & - & 100\% (41/41) \\ \hline \end{tabular}
\end{table} 
Table 13: Accuracy of the created decision tree w.r.t. interaction-curvature predictor selection on test set data for set data for 80% training and 20% testing. \begin{table}
\begin{tabular}{c} Curvature \\ Interaction- \\ curvature \\ \end{tabular} & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Confusion matrix performance. Figure 17: Confusion matrix for training (**a**) and testing (**b**) performance of decision tree. The fault due to lightning when tested with 30% and 20% data set shows the best performance (100%) for all predictor selection except for interaction-curvature where the accuracy, specificity, precision, and F1 score are 98.8%, 94.5%, 94.8%, and 97.3%, respectively. In addition, the same pattern was obtained for fault due to insulator degrading where interaction-curvature gives the worst performance as compared with the other predictor selection. The accuracy of the confusion matrix is 99.6%, with specificity is 99.4%, precision is 98.6% and F1 score is 99.3%. As for the tree encroachment, more misclassified data were obtained as compared to the lightning and insulator degradation where the confusion matrix performance is also lower. Based on the table, curvature predictor selection shows the worst performance when the tested sample is 30%, while all splits and interaction-curvature shows low performance when the tested sample is 20%. However, among all of them, interaction-curvature gives the worst predictor selection performance with only 98.2% accuracy, 97.8% specificity, 91.4% precision, and 95.5% F1 score.