## 5 Deep Learning Model
### Analysis and Results  
This section analyzes the deep learning model for selecting suitable requirement elicitation techniques. In this model, we intend to analyze the performance of the deep learning model using the confusion matrix. The model started by preprocessing and scaling the dataset using Scikit-learn, the machine learning library for python programming. The dataset comprises 1684 technique selection attribute samples for the 14 elicitation techniques. These samples will be split into two subsets to estimate the model performance. The first subset was used to train the model in 70% (1178) samples of dataset records. The second subset will be used for testing purposes in 30% (506) samples of dataset records to compare the model prediction with the expected. Figure 3 illustrates the confusion matrix report of the deep learning model for the 506 testing samples. There are cases the model predicted yes for the 14 classified elicitation techniques (true positives (TP)); predicted no (true negatives (TN)); falsely predicted yes (false positives (FP)); and falsely predicted no (false negatives (FN)). These four metrics will allow us to calculate the performance metrics (such as precision to measure the model's ability to return only the data points in a class as follows in Equation (5); recall to measure the model's ability to identify all data points in a relevant class as follows in Equation (6); F1 score to reflect how reliable the model is in classifying samples, as follows in Equation (7); and accuracy to measure the model the ratio of correctly predicted elicitation technique, as follows in Equation (8) and as shown in Table 3). \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
\[\text{F1 score}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{7}\]  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{8}\]  
Figure 3: Confusion matrix and classification report. Table 3 illustrates the deep learning performance metrics results, including the number of occurrences of each particular class in the true responses. As we can see in the support column of Table 3, the model was able to correctly identify actual incidences of the 14 elicitation techniques in a good ratio. Next, the results revealed the model's ability to return more relevant results than irrelevant ones, which is reflected by the macro-average precision score of 0.83 computed without considering the proportion and the weighted-average precision score of 0.83 computed by taking the mean of all per-class support relative to the sum of all support values. Next, the model returned the most relevant results, reflected by the macro-average recall score of 0.83 computed without considering the proportion, and a weighted-average recall score of 0.82 compute by taking the mean of all per-class support relative to the sum of all support values. Moreover, the model revealed a relatively higher macro-average F1 score of 0.82 computed without considering the proportion, and a weighted-average F1 score of 0.82 compute by taking the mean of all per-class support relative to the sum of all support values. Finally, calculating the accuracy of the prediction that was made correctly by the model. The results revealed a relatively higher accuracy ratio of 0.82 representing the model's prediction ability and efficiency in nominating a suitable elicitation technique based on the elicitation case scenario. Model Validation  
This section summarizes the evaluation metrics used to validate the model prediction. The deep learning model was validated using a loss curve and the area under the ROC curve (AUC).