## 4 Results and Discussion
### Machine Learning Models
The neural network comprising 100 neurones in the initial layer and the ReLU activation function demonstrated remarkable efficacy in numerous categories. The NN model demonstrated the highest accuracy, at 94.7%, and was therefore the most effective in terms of prediction accuracy. Furthermore, the neural network exhibited the lowest error rate of 5.3%, indicating the lowest number of misclassifications. In addition, the NN model exhibited an impressive prediction speed of 7800 observations per second, making it the fastest model in the analysis. The training time was 8.39 s, which, although not the shortest, was sufficiently rapid given the complexity of the model. The Support Vector Machine (SVM) was utilised with a Gaussian kernel having a scale of 0.75, which allowed the creation of intricate decision boundaries. The most significant advantage of this model was its exceptionally short training time of just 1.67 s, making it the most time-efficient model. However, the SVM demonstrated suboptimal performance in terms of prediction precision, achieving an accuracy of 94%, which was significantly different from the results of NN and BT. The error rate for SVM was 6%, indicating a higher number of misclassifications compared to the other models. The neural network (NN) was identified as the most comprehensive and efficient model, exhibiting the highest accuracy, lowest error rate, and fastest prediction speed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Results** & **BT** & **NN** & **SVM** \\ \hline Prediction speed [obs./sec] & 1400 & 7800 & 5900 \\ \hline Training time [sec] & 21.02 & 8.39 & 1.67 \\ \hline \multirow{4}{*}{Model Hyperparameters} & Preset: Bagged Trees & Preset: Wide Neural Network & \multirow{2}{*}{\begin{tabular}{c} Preset: Fine-Gaussian SVM \\ Kernel scale: 0.75 \\ Kernel function: Gaussian \\ Kernel function: Gaussian \\ \end{tabular} } \\  & Ensemble method: Bag & First layer: 100 & \multirow{2}{*}{\
\begin{tabular}{c} Preset: Fine-Gaussian SVM \\ Kernel scale: 0.75 \\ Kernel function: Gaussian \\ \end{tabular} } \\  & Learner type: Decision Tree & Activation: ReLU & \multirow{2}{*}{
\begin{tabular}{c} Preset: Fine-Gaussian SVM \\ Kernel scale: 0.75 \\ Kernel function: Gaussian \\ \end{tabular} } \\  & Maximum number of splits: 564 & Iteration limit: 1000 & \\  & Number of learners: 30 & & \\ \hline Accuracy & 94.2 & 94.7 & 94.0 \\ \hline Error Rate & 5.8 & 5.3 & 6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The best models, training parameters. Although the training time was not the shortest, it was sufficiently rapid to allow the NN to outperform the other models in terms of overall efficiency. Despite demonstrating a level of accuracy similar to that of the NN, the BT exhibited slower processing times and a reduced capacity for real-time operation. Consequently, it is less suitable for applications that require rapid response times. On the contrary, the SVM exhibited the fastest training times but exhibited lower accuracy and a higher error rate, limiting its applicability to scenarios where training speed is a primary consideration. In such cases, the NN would be the optimal choice for accurate and rapid prediction. The performance of each machine learning model was evaluated using confusion matrices, which are a key tool in this context. These matrices provide a comprehensive breakdown of the performance of the models by comparing the actual class labels (true labels) with the predicted labels generated by the models. This analysis enables not only the evaluation of the overall accuracy of the models but also gains a deeper understanding of the types and frequencies of specific errors made by each model. This allows for a detailed assessment of the models' strengths and weaknesses, highlighting areas where improvements may be necessary, such as identifying which classes tend to be misclassified and the severity of such errors. Figure 5 presents the confusion matrices for each machine learning model developed.