Removing low influential samples, on the other hand, does not decrease performance but instead increases it until a certain point, similar to random sampling. This is caused by the fact that removing uninfluential samples improves the imbalance of the dataset, thereby lowering its bias to the majority class without worsening its prediction performance, as these samples did not help the model learn its task. Interestingly, random undersampling performs very similarly to removing low influential samples. This is because most inactive samples end up being uninfluential, and only a tiny set of inactives is essential for the model to learn its classification task, comparable to the support vectors of a support vector machine. Therefore, if the random undersampling method does not accidentally remove these few samples, it will perform similarly to the low-influence removal method. As a result, especially when removing large parts of the training set randomly, we see an increase in deviations between replicates, as some replicates randomly remove important samples while others do not. In comparison, the error bars of the influence-based undersampling do not show this increased effect.