## 4 Experiment and Results
### Multiclass Classification
Figure 5: Confusion matrix–Random Forest. \begin{table} \begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & \begin{tabular}{l} **F1-** \\ **Score** \\ \end{tabular} & \begin{tabular}{l} **Searching** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{l} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{l} **Testing** \\ **Time (s)** \\ \end{tabular} \\ \hline RF & 0.91 & 0.90 & 0.91 & 0.90 & 1547.43 & 16.21 & 1.15 \\ LR & 0.79 & 0.79 & 0.79 & 0.75 & 1834.67 & 8.00 & 0.01 \\ DT & 0.91 & 0.91 & 0.91 & 0.90 & 9.24 & 0.12 & 0.01 \\ GB & 0.91 & 0.91 & 0.91 & 0.90 & 24,489.46 & 505.96 & 4.80 \\ XGBoost & 0.91 & 0.91 & 0.91 & 0.90 & 49,323.92 & 15.75 & 0.06 \\ MLP & 0.82 & 0.85 & 0.82 & 0.80 & 3573.60 & 141.93 & 0.11 \\ DNN & 0.78 & 0.77 & 0.78 & 0.74 & 5081.92 & 702.20 & 6.03 \\ \hline \hline \end{tabular}  (b) Confusion matrix: As previously discussed, the confusion matrix highlights the frequency of correct and incorrect predictions made by a model by comparing actual classifications to predicted classifications. Multiclass classification becomes more complex than binary classification. Instead of a simple 2 \(\times\) 2 grid, the matrix expands to an N \(\times\) N grid, where N represents the number of classes. Each cell in this grid represents the count of instances for each actual/predicted class pair, thus helping us to understand the model’s performance across all six classes, showing how many instances of each class are correctly or incorrectly predicted. Table 9 is provided to facilitate the interpretation of the confusion matrix for each model, where the following definitions apply: - TP (XX): True positive for class X. - FP (XY): False positive, predicted as class Y but actual class is X. - FN (YX): False negative, actual class Y but predicted as class X. For the sake of brevity, we present Figure 5, the confusion matrix for the RF model. The confusion matrices for the other six models are provided in the Appendix A (see Figure A1). \begin{table} \begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & \begin{tabular}{l} **F1-** \\ **Score** \\ \end{tabular} & \begin{tabular}{l} **Searching** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{l} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{l} **Testing** \\ **Time (s)** \\ \end{tabular} \\ \hline RF & 0.91 & 0.90 & 0.91 & 0.90 & 1547.43 & 16.21 & 1.15 \\ LR & 0.79 & 0.79 & 0.79 & 0.75 & 1834.67 & 8.00 & 0.01 \\ DT & 0.91 & 0.91 & 0.91 & 0.90 & 9.24 & 0.12 & 0.01 \\ GB & 0.91 & 0.91 & 0.91 & 0.90 & 24,489.46 & 505.96 & 4.80 \\ XGBoost & 0.91 & 0.91 & 0.91 & 0.90 & 49,323.92 & 15.75 & 0.06 \\ MLP & 0.82 & 0.85 & 0.82 & 0.80 & 3573.60 & 141.93 & 0.11 \\ DNN & 0.78 & 0.77 & 0.78 & 0.74 & 5081.92 & 702.20 & 6.03 \\ \hline \hline \end{tabular}  (b) Confusion matrix: As previously discussed, the confusion matrix highlights the frequency of correct and incorrect predictions made by a model by comparing actual classifications to predicted classifications. Multiclass classification becomes more complex than binary classification. Instead of a simple 2 \(\times\) 2 grid, the matrix expands to an N \(\times\) N grid, where N represents the number of classes. Each cell in this grid represents the count of instances for each actual/predicted class pair, thus helping us to understand the model’s performance across all six classes, showing how many instances of each class are correctly or incorrectly predicted. Table 9 is provided to facilitate the interpretation of the confusion matrix for each model, where the following definitions apply: - TP (XX): True positive for class X. - FP (XY): False positive, predicted as class Y but actual class is X. - FN (YX): False negative, actual class Y but predicted as class X. For the sake of brevity, we present Figure 5, the confusion matrix for the RF model. The confusion matrices for the other six models are provided in the Appendix A (see Figure A1).