Previously, false positives were identified either via their high influence on their own prediction or their low influence on the prediction of other samples, dependent on the influence method. Conversely, the opposite approach can be used to identify true positives. True positive samples will have a high influence on the prediction of other samples, as the machine learning model uses these true positives to recognize patterns. Their self-influence, however, will be much lower than false positives, as their activity can also be inferred from other true positives, and a true positive's prediction is therefore not strongly dependent on its own presence in the training set.