KNN Shapley is an approximation of the cooperative game theory concept of Shapley Values (SVs). Classical SVs are calculated by computing the utility of a machine learning model of every possible subset of the training set with and without a sample of interest, calculating the difference, and averaging it over all subsets. However, this calculation is impractical for large datasets, as the number of subset calculations scales exponentially. KNN Shapley applies the concept of SVs only within the neighborhood of a validation set sample. As a result, many subsets can be ignored when calculating Shapley values, leading to greater computational efficiency. When applying the KNN Shapley values to a binary classification problem, the minority class samples are expected to be assigned higher values, as they are less prevalent, and their effect on the subset prediction is, therefore, greater. This is especially true for HTS data due to the high imbalance. Within the active class, true positives tend to be more influential than false positives, as their influence is computed on a separate test set. A true positives' absence worsens the prediction of true positives in the test set due to their likely similarity. In contrast, the absence of false positives does not negatively impact the test set prediction, resulting in false positives being assigned lower Shapley values.