## 3 Materials and Methods
The ReLU activation function (\(\sigma\)) can be represented by the following Equation (1):  
\[\sigma(x)=\max\,(x,0), \tag{1}\]  
where \(x\) represents the input. The classification tasks in ML models can also be broadly categorized based on the number of outputs and the range of possible values for each output. Binary classification is the process of assigning instances to one of two possible classes for a single output variable. In this context, the functional class requirements are characterized by the presence of distinguishing features that serve to differentiate them from non-functional class requirements [65]. In contrast, multi-class classification extends this by allowing the single output variable to belong to one of several distinct classes, thus offering a more nuanced approach to categorization [66]. In multi-class classification, individual classes of requirements often have few distinguishable features, which can result in challenges in terms of predictor performance [65]. Shapley values, as initially defined by [PERSON] [67], represent a classical concept in cooperative game theory. These values have recently been applied to the interpretation of ML models' predictions [68]. Shapley values can also be interpreted as representing an equitable or reasonable distribution of feature importance, given a specific model output. Features contribute to the output or prediction of the model to a different degree, both quantitatively and qualitatively. This phenomenon is accommodated by the use of Shapley values. In this manner, the Shapley values can be considered as estimates of feature import, i.e., the magnitude of a feature's contribution to the prediction, as well as the direction of this contribution, i.e., its sign. Features with a positive sign contribute positively to the prediction of activity, whereas features with a negative sign contribute negatively to the prediction of inactivity [69]. The importance of an input \(i\) is defined by the Shapley value as in Equation (2):  
\[\varphi_{i}=\frac{1}{|N|!}\!\!\sum_{S\in N\{i\}}|S|!(|N|-|S|-1)![f(S\cup\{i\}) -f(S)], \tag{2}\]  
where \(f(S)\) corresponds to the output of the ML model to be explained using a set \(S\) of inputs, and \(N\) is the complete set of all inputs. The Shapley value of input \(i\) (\(\varphi_{i}\)) is determined as the average of its contributions across all possible permutations of an input set. The performance of ML algorithms is evaluated using a number of different metrics, including accuracy, precision, recall, F1-score, and kappa statistic. Accuracy is the proportion of instances that are correctly classified, precision is the proportion of instances classified as positive that are truly positive, recall is the proportion of positive instances that are correctly classified as positive, and the F1-score combines the precision and recall [55]. The following equations (Equations (3)-(6)) are used for the accuracy, precision, recall, and F1-score metrics:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F1-score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{6}\]  
where true positive (\(TP\)) and true negative (\(TN\)) represent the correctly classified instances, and false positive (\(FP\)) and false negative (\(FN\)) represent the incorrectly classified instances.