## 5 Results
### Diagnosis and Prognosis of Fault  
The diagnostic outcomes underscore the effectiveness of the AdaBoost ensemble utilizing Decision Trees as weak classifiers in accurately identifying fault statuses. With an impressive 88% overall accuracy, the model exhibits notable precision, recall, and F1-scores across diverse fault categories, particularly excelling in identifying 'Healthy' and 'Two Half Teeth' cases. However, it faces challenges in accurately classifying 'One-Half Tooth' instances, displaying perfect precision but relatively lower recall, leading to a reduced F1-score. Notably, the model struggles with the 'Two Teeth' category, failing to make accurate predictions. Nevertheless, the weighted average F1-score of 0.92 indicates robust overall performance. Figure 10 showcasing the classification performance of the AdaBoost ensemble model using a confusion matrix. Each cell represents the number of samples classified into a particular fault status, aiding in classification accuracy assessment. The right subplot illustrates the feature's importance, indicating the contribution of each feature in predicting fault statuses. This comprehensive analysis provides insights into the model's diagnostic efficacy and the significance of different features in fault diagnosis.  
The mean squared error (MSE) of 1.12 shows how well the AdaBoost ensemble can guess the system's remaining useful life (RUL). Lower MSE values mean that the predicted and actual RUL values are more closely related. Overall, the AdaBoost ensemble with Decision Trees as weak classifiers emerges as a promising technique for fault diagnosis and prognosis, offering accurate insights into system health and future performance.  
Figure 11 shows the relationship between the actual remaining useful life (RUL) values and the predicted RUL values generated by the AdaBoost regressor for prognosis. Each blue point represents an individual sample from the test dataset, where the x-coordinate corresponds to the actual RUL and the y-coordinate represents the predicted RUL. The  
Figure 10: Diagnostic performance and feature importance analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Healthy & 1.00 & 1.00 & 1.00 & 2 \\ One-Half Tooth & 1.00 & 0.50 & 0.67 & 2 \\ One Tooth & 1.00 & 1.00 & 1.00 & 1 \\ Two Half Teeth & 1.00 & 1.00 & 1.00 & 3 \\ Two Teeth & 0.00 & 0.00 & 0.00 & 0 \\ \hline
**Accuracy** & \multicolumn{4}{c}{0.88} \\
**Macro Avg** & \multicolumn{4}{c}{0.80 0.70 0.73 8} \\
**Weighted Avg** & \multicolumn{4}{c}{1.00 0.88 0.92 8} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance Metrics of AdaBoost Classifier with Decision Tree as Weak Classifier.  
red dashed line indicates the line of perfect prediction, where the predicted RUL perfectly matches the actual RUL. Additionally, a green line, added as a line of best fit, illustrates the trend of the predicted RUL values concerning the actual RUL values. This line provides insights into the overall accuracy of the predictions and the direction of any systematic deviations between predicted and actual values. Including the green line in the legend enhances the graph's clarity by explicitly stating its purpose.