## 6 Exploration of Findings
Notably, the RFC also had the shortest training time of just 0.82 s.  
Figure 7 presents a detailed analysis of feature significance by comparing the results obtained from the RFC and a permutation analysis approach. The features are sorted in ascending order based on their importance, as determined by the RFC, as shown in Figure 7a. In contrast, Figure 7b depicts the permutation analysis of the same features. The permutation analysis assesses the impact of shuffling each feature on model accuracy. To  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-Score**} \\ \hline
**Classifier** & **RFC** & **ANN** & **SVC** & **RFC** & **ANN** & **SVC** & **RFC** & **ANN** & **SVC** \\ \hline No-load & 1.000 & 1.000 & 0.868 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 0.929 \\ \hline Full-load & 0.997 & 0.965 & 1.000 & 0.995 & 0.768 & 0.000 & 0.996 & 0.855 & 0.000 \\ \hline Front Defect (2 mm) & 1.000 & 0.996 & 1.000 & 0.999 & 0.955 & 0.000 & 0.999 & 0.975 & 0.000 \\ \hline Front Defect (5 mm) & 1.000 & 0.995 & 1.000 & 0.997 & 0.997 & 0.000 & 0.999 & 0.996 & 0.000 \\ \hline Rear Defect (2 mm) & 0.994 & 0.680 & 1.000 & 0.986 & **0.165** & 0.000 & 0.990 & **0.266** & 0.000 \\ \hline Rear Defect (5 mm) & 1.000 & 0.813 & 1.000 & 0.982 & 0.644 & 0.000 & 0.991 & 0.718 & 0.000 \\ \hline Front and Rear Defect (2 mm) & 1.000 & 0.992 & 1.000 & 0.996 & 0.978 & 0.000 & 0.998 & 0.985 & 0.000 \\ \hline Front and Rear Defect (5 mm) & 0.999 & 0.870 & 1.000 & 0.983 & 0.781 & 0.000 & 0.991 & 0.823 & 0.000 \\ \hline Single-Phase Fault & 0.970 & 0.987 & 0.957 & 0.985 & 0.900 & 0.605 & 0.977 & 0.941 & 0.741 \\ \hline micro avg & 0.997 & 0.946 & 0.885 & 0.992 & 0.797 & 0.154 & 0.995 & 0.865 & 0.263 \\ \hline macro avg & 0.996 & 0.922 & 0.981 & 0.991 & 0.799 & 0.178 & 0.993 gether, these visualizations provide a comprehensive understanding of feature significance. Notably, when focusing on the most important features, both methods consistently identify the same five dominant features: the THD of phase 1 current (ITHD_ph1), THD of phase 2 voltage (VTHD_Ph2), fifth harmonic of phase 1 current (IIS_1_5 th), the third harmonic of phase 2 current (IIS_2_3 rd), and the fifth harmonic of phase 3 current (IIS_3_5 th), albeit with slight variations in their order. Despite this consistency, permutation analysis reveals that four of these features exhibit an average reduction in accuracy of approximately 0.07%, while only one feature shows a more tangible deterioration in accuracy of 0.12%. In contrast to the RFC's feature importance, permutation analysis reveals the insignificance of other features on the overall performance of the predictive model, which emphasizes the issue of multicollinearity among correlated datasets. This redundancy underscores the need for more intelligent feature selection algorithms. To thoroughly investigate the presented methodology, the following subsections cover three cases. The first case evaluates the performance of three classifiers, SVC, ANN, and RFC, using the top twenty-three dominant features identified by the RF feature importance metric, while the second case reports the performance of these classifiers using the twenty-three features selected by the AHC algorithm. Lastly, the third case involves a comparative analysis based on feature importance.