# Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
## 4 Results and Discussion  
The parameter from Table 5 above was used to train the classifier, and the best test accuracy was achieved at 748 iterations out of 1000, which is 99.54% with an error of 0.46%. This result confirmed that the classifier model worked perfectly, and the different types of faults were trained and classified with high accuracy. The no-fault condition was trained separately, and an accuracy of 100% was obtained. This was trained separately to attain a near-perfect classification due to the complexity of the dataset. Table 6 represents the classifier's confusion matrix, which describes the precision, recall, F1-score, and support. An \(N\times N\) matrix was often used to evaluate the performance of the classifier model, where \(N\) was the number of target classes. The matrix compared the actual target value with the predicted machine learning model and the error involved. The table shows that the no-fault condition represented 0, the single line to ground fault was 1, the double line to ground was 2, and the three-phase to ground fault was 3. Class 0 was kept at zero because it was at a no-fault condition while the others were trained. The result shows that the model was well fitted, and the four different fault types were well classified. \begin{table}
\begin{tabular}{l l} \hline \hline
**CatBoost Model is Fitted** & **True** \\ \hline Iterations & 1000 \\ Depth & 10 \\ Loss function & Multiclass \\ Leaf estimation method & Newton \\ Class weight & 0.001, 0.01, 0.9, 0.001 \\ Random strength & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CatBoost Classifier training parameter. The accuracy of the model is given as  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]  
where \(TP\) = True positive; \(TN\) = True Negative; \(FP\) = False Positive; and \(FN\) = False Negative.Furthermore,,  
\[\text{Precision}=\frac{TP}{TP+FP}. \tag{4}\]  
tells how many of the predicted cases turned out to be positive, and determines whether the model was reliable. In Table 6, the precision in single-phase to ground and three-phase fault was 1, which showed that the model was worked perfectly well. 'Recall' shows how many of the actual positive cases were predicted correctly and is given by  
\[\text{Recall}=\frac{TP}{TP+FN}. \tag{5}\]  
The double line to ground fault was predicted correctly compared to other faults, as shown in Table 6. Also, the F1-Score was the harmonic mean of precision and recall and is given by:  
\[\text{F1}-\text{Score}=\frac{2}{\frac{1}{\text{Recall}}+\frac{1}{\text{Precision}}} \tag{6}\]  
True-positive indicated that the classifier predicted a true event, and the event was true, whereas true-negative indicates that the classifier predicted a false event, and the event was false. The false positive classifier predicted that an event would occur, but it was incorrect. Still, the event was not true, whereas the false-negative indicated that the event was incorrectly predicted and was therefore false. The results from the fault classification report in Table 6 also affirmed that the classifier produced perfect results. Therefore, it was a better classifier for training multi-datasets than were those from the reviewed literature. Table 7 compares the different machine-learning techniques used in fault classification based on various methods and justifies the use of the algorithm, focusing on accuracy, speed, and strength. The CatBoost classifier produced a better result than did the other classifiers, as seen in Table 7, with an accuracy of 99.54%. The CatBoost technique was chosen over other methods for its speed, accuracy and low training time for classifying faults according to different categories. It can also accurately handle multi-datasets of different fault currents and voltage at the same time.