The above result suggests that the undersampling facilitates the training of the machine learning models by eliminating bit collisions. The better prediction performance of undersampled models with remaining bit collisions than simple bit collision elimination for some target compounds suggests that NEIMS, which has a fully connected neural network structure that learns by considering all bits, can learn even if there are some bit collisions.