## 3 Results
### Results for Boosting Ensemble Decision Trees  
The parameters used for training boosting ensemble decision trees are as follows:  
* Method: AdaBoostM2--boosting method for multi-class classification.
* NumLearningCycles: 96--the number of learning cycles in the ensemble.
* LearnRate: 0.58881--the learning rate for the ensemble.
* MinLeaSize: 5--the minimum number of observations per leaf in the decision tree.
* Hyperparameter optimization: Bayesian optimization.  
The confusion matrix depicted in Figure 5 shows the performance of the optimized boosting ensemble decision trees classification model, applying a boosting approach with 100 trees for the three wear classes used for classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** & **Accuracy** \\ \hline Green & 100.00\% & 100.00\% & 100.00\% & 100.00\% & 100.00\% \\ Yellow & 95.83\% & 92.00\% & 98.00\% & 93.87\% & 96.00\% \\ Red & 92.30\% & 96.00\% & 96.00\% & 94.11\% & 96.00\% \\ \hline Overall & & & & & 96.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics of the SVM model, denoting the key parameters used for model evaluation for each of the recognized classes.  
For the green class, the model exhibits commendable accuracy, with the majority of instances correctly classified, reflecting a high sensitivity rate of 96%. The precision for this class stands at 88.88%, indicating a relatively lower, but still substantial, likelihood of instances being accurately identified as green out of all instances predicted as green.  
The yellow class presents a challenge with a sensitivity of 84%, showing some instances of misclassification. The precision rate for yellow, at 87.50%, indicates that while the model can identify yellow instances with reasonable accuracy, there is room for improvement in reducing false positives.  
The red class showcases a sensitivity of 88% and a precision of 91.66%, indicating a strong ability of the model to identify red instances accurately, albeit with a small margin of error.  
Table 3 further details the performance metrics of the boosting ensemble decision trees model. It highlights the model's strengths in specific areas, such as high precision and sensitivity for the red class. It identifies areas for improvement, such as the slightly lower sensitivity for the yellow class.  
Overall, the optimized boosting ensemble decision trees model demonstrates robust performance with an overall accuracy of 89.33%. The model effectively balances precision and sensitivity across classes, making it a reliable tool for classification tasks in this context. Future work may focus on enhancing the model's sensitivity for the yellow class to improve its applicability and effectiveness further.  
Figure 5: The confusion matrix for optimized boosting ensemble decision trees. Numbers off the diagonal denote cases that were not correctly classified. The colors represent the proportions of the examples, further visualizing the number of cases for each category, with lighter values denoting lower counts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **Precision** & **Sensitivity** & **Specificity** & **F1 Score** & **Accuracy** \\ \hline Green & 88.88\% & 96.00\% & 94.00\% & 92.30\% & 94.66\% \\ Yellow & 87.50\% & 84.00\% & 94.00\% & 85.71\% & 90.66\% \\ Red & 91.66\% & 88.00\% & 96.00\% & 89.79\% & 93.33\% \\ \hline Overall & & & & & 89.33\% \\ \hline \end{tabular}
\end{table}
Table 3: Performance metrics of the optimized boosting ensemble decision trees, denoting the key parameters used for model evaluation for each of the recognized classes.