## 4 Results  
Table 3 presents a comparative analysis of various classification algorithms for fault detection in rotating machinery, evaluated based on accuracy, precision, recall, F1 scores, and execution times. Among the tested models, the Random Forest stood out not only in classification accuracy (0.768) but also in efficiency, with a notably fast execution time of 0.953 s, followed closely by CatBoost (0.765) and extra trees (0.755). However, the CatBoost model showed a disadvantage in terms of execution time. These ensemble techniques seem to be more effective than the remaining models, which show comparably better performance in fault classification tasks. In contrast, simpler models, such as the ridge classifier (0.624), K-Nearest Neighbors (0.676), and Linear SVC (0.670), reported average accuracy, which suggests that these algorithms could not handle the complexity or non-linearity of the given dataset. The boosting algorithms LightGBM (0.752), bagging (0.743), and Gradient Boosting (0.731) demonstrated varied levels of performance. Their performances were slightly lower than any of the aforementioned ensemble methods, likely due to greater sensitivity to noise in the dataset. In contrast, Gradient Boosting (0.731), XGBoost (0.715), and the SVM (0.713) achieved moderate performance, indicating their effectiveness but slightly lower generalization capabilities. On the lower end, the decision tree (0.682), K-Nearest Neighbors (0.676), Linear SVC (0.670), and SGD (0.627) displayed average classification abilities, while AdaBoost (0.532) performed the worst, likely due to its sensitivity to noisy data. Overall, the results highlight that ensemble-based models, particularly the Random Forest, are the most effective for fault detection in the given rotating machinery, offering relatively higher accuracy and reliability. Figure 6: Correlation heatmap. After performance measurement with default values, hyperparameter optimization was conducted for all classifiers using grid search with 5-fold cross-validation. The tuned results are summarized in Table 4. Among the models, the Random Forest achieved the highest classification accuracy (0.771). The Random Forest classifier performs considerably better because it is an ensemble learning method composed of many decision trees. This structure reduces the probability of overfitting compared to individual trees and enhances robustness and stability when handling complex datasets. It brings in randomness at two levels, namely at the level of training data and at the level of features used to build each tree, which ensures diversity in trees and hence better generalization. Besides that, the Random Forest has the capability of evaluating the importance of individual features, which can guide feature selection and improve efficiency. It can cope with large datasets featuring many attributes and avoids overfitting by averaging predictions across multiple trees, making it extremely valuable for handling noisy or high-dimensional data. Right after the Random Forest, CatBoost (0.769) and Gradient Boosting (0.763) followed closely. Notably, Gradient Boosting and the bagging classifier, after tuning, showed a marked improvement to an accuracy of 0.763 and 0.761, indicating that they may compete closely with the Random Forest. While CatBoost and XGBoost performed well in terms of accuracy, their significantly higher execution times (870.3 s and 306.5 s, respectively) may limit their suitability for time-sensitive applications. Simpler models such as the decision tree, K-Nearest Neighbors, and the SVM benefited modestly from optimization, yet they were still outperformed by ensemble and boosting techniques. AdaBoost, despite tuning, remained the least effective model, likely due to its sensitivity to noise. These results confirm the importance of hyperparameter tuning and demonstrate that ensemble-based methods, particularly the Random Forest and the bagging classifier, provide robust performance for multi-sensor fault classification tasks under real-world industrial conditions.