## 4 Results
A comprehensive range of values was evaluated for each parameter, as presented in Table 5, which also summarizes optimal hyperparameter settings for the Random Forest classifier. These values were selected based on their average validation accuracy scores across the five cross-validation folds. The model performed best with 1000 estimators, indicating that increasing the number of decision trees improves model stability and accuracy by significantly reducing variance. In the Random Forest algorithm, the _max_depth_ parameter defines the maximum depth each individual decision tree can grow. Setting it to 10 strikes a reasonable balance between model complexity and the risk of overfitting: \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Classifier Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Execution** \\  & & & & & **Time (s)** \\ \hline Random Forest & 0.771 & 0.761 & 0.771 & 0.764 & 23.82 \\ CatBoost & 0.769 & 0.761 & 0.769 & 0.764 & 870.3 \\ Gradient Boosting & 0.763 & 0.756 & 0.763 & 0.757 & 160.3 \\ Bagging Classifier & 0.761 & 0.754 & 0.761 & 0.757 & 4.35 \\ Extra Trees & 0.758 & 0.757 & 0.758 & 0.755 & 27.33 \\ LightGBM & 0.756 & 0.743 & 0.756 & 0.753 & 120.9 \\ XGBoost & 0.752 & 0.748 & 0.752 & 0.749 & 306.5 \\ SVM & 0.735 & 0.729 & 0.735 & 0.727 & 9.46 \\ K-NN & 0.731 & 0.734 & 0.731 & 0.726 & 1.22 \\ Decision Tree & 0.703 & 0.706 & 0.703 & 0.703 & 0.86 \\ Linear SVC & 0.703 & 0.702 & 0.703 & 0.691 & 0.85 \\ SGD & 0.697 & 0.717 & 0.697 & 0.697 & 0.79 \\ Ridge & 0.640 & 0.636 & 0.640 & 0.607 & 0.53 \\ AdaBoost & 0.561 & 0.557 & 0.561 & 0.517 & 5.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of classification models after hyperparameter optimization. deeper trees may overfit the training data, while shallower trees may underfit and fail to capture relevant patterns. The selected values for \(min\_samples\_split=2\) and \(min\_samples\_leaf=1\) indicate that the model is capable of splitting nodes as long as there are at least two samples available, with leaf nodes containing at least one sample. This configuration allows for more detailed splits, thereby enhancing the model's ability to capture complex patterns without sacrificing accuracy. Additionally, setting \(max\_features=sqrt\) ensures that feature selection is performed randomly, which introduces diversity and mitigates the risk of overfitting. By setting the bootstrap parameter to True, the model can utilize bootstrapped sampling, a core feature of the Random Forest in variance reduction. The Gini criterion was chosen as a measure of the impurity of the nodes, which reflects class homogeneity in the splitting. There were no inputs such as class_weight and max_samples that might signify a balanced dataset that would not require weighting. To summarize, these hyperparameter values exemplify a well-tuned Random Forest model optimally aligned for stability and generalization. The trees are relatively deep. The number of estimators is high, and the feature selection is random. Together, they contribute to the robustness of the model in classifying faults. To further validate the impact of hyperparameter optimization, we conducted a paired t-test comparing the 5-fold cross-validation accuracy scores of the default and optimized Random Forest models. Prior to the test, the normality of the differences was assessed using the Shapiro-Wilk test (\(p=0.4217\)), confirming that the distribution of differences satisfies the normality assumption. The paired t-test revealed a statistically significant improvement in accuracy (\(t=-3.76\), \(p=0.0197\)), based on a significance level of 0.05, supporting the effectiveness of the optimization strategy. The SHAP-based feature importance analysis (Figure 7) provides a detailed understanding of how individual variables contribute to the classification performance across different fault categories.