## 5 Deep Learning Model
### Analysis and Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attributes** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
1 & 0.83 & 0.83 & 0.82 & 30 \\ \hline
2 & 077 & 0.83 & 0.80 & 36 \\ \hline
3 & 0.88 & 0.76 & 0.82 & 38 \\ \hline
4 & 0.69 & 0.89 & 0.78 & 37 \\ \hline
5 & 0.74 & 0.65 & 0.69 & 43 \\ \hline
6 & 0.70 & 0.74 & 0.72 & 38 \\ \hline
7 & 0.76 & 0.70 & 0.73 & 40 \\ \hline
8 & 0.82 & 0.75 & 0.78 & 36 \\ \hline
9 & 1.00 & 0.83 & 0.91 & 35 \\ \hline
10 & 0.86 & 1.00 & 0.92 & 30 \\ \hline
11 & 0.86 & 0.86 & 0.86 & 36 \\ \hline
12 & 0.94 & 0.86 & 0.90 & 35 \\ \hline
13 & 0.79 & 0.94 & 0.86 & 36 \\ \hline
14 & 1.00 & 0.75 & 0.86 & 36 \\ \hline Accuracy & & & 0.82 & 506 \\ \hline Macro avg & 0.83 & 0.83 & 0.82 & 506 \\ \hline Weighted avg & 0.83 & 0.82 & 0.82 & 506 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix and classification report for the training dataset. Loss Curve  
In this model, we implement the Mean Squared Error (MSE) as follows in Equation (9) to measure the amount of error in the deep learning model by calculating the average squared difference between the actual and predicted data point values. \[\text{MSE}=\frac{1}{\text{n}}\,\sum_{i=1}^{n}\bigl{(}\mathbf{Y_{i}}-\hat{ \mathbf{Y}_{i}}\bigr{)}^{2} \tag{9}\]  
where \(\mathbf{Y_{i}}\) is actual data point values, \(\hat{\mathbf{Y_{i}}}\) is the predicted data point values and \(\mathbf{n}\) is the total number of data point in the dataset. Figure 4 shows the loss curve of the deep learning model for over 80 iterations. In contrast, the cost value decreases with every iteration during the neural network training session. Therefore, reflecting the learning performance over time in terms of experience. Finally, the cost value reached fewer than 0.3 points, which is considered an acceptable MSE score. The area under the ROC curve (AUC) was used to calculate the deep learning model prediction accuracy. Thus, we computed the k-Fold cross-validation to maximize the use of the available dataset for training and assessing model performance. The key configuration parameter for k-fold cross-validation is k = 10, which defines the number folds in which to split a given dataset. The reason for this is that studies were performed and k = 10 was found to provide good trade-off of low computational cost and low bias in an estimation of model performance. Thus, our model scored 0.82 (\(\pm\)0.10), reflecting less statistical noise and hence a more reliable model performance. Table 4 shows the One-vs-Rest and One-vs-One for multi-class classification scores of the deep learning model. In order to provide an aggregate measure of performance across all possible classification thresholds, One-vs-Rest was computed by comparing each class against all of the others at the same time. Next, One-vs-One was computed by comparing all possible two-class combinations of the dataset, thereby splitting the multi-class classification dataset into binary classification. The results illustrated in Table 4 confirm the model's ability to distinguish between the elicitation techniques with a score of 0.75. Figure 4: Loss curve. Figure 5 shows the areas under the multiclass ROC curves (AUC), evaluating the model's classification ability. Each colored line of the figure represents a specific elicitation technique in the model. The distribution of elicitation technique values between a false positive rate of zero and a true positive rate of one evidently illustrates the model's capacity to clearly distinguish between these techniques, confirming the model's ability to provide an accurate range of predictions.