As a baseline model, we trained a 1-nearest neighbors (1-NN) classifier corresponding to a perfect memorization of the training set. The data shows that an impressive-looking 86% accuracy can be achieved for the test set by simple memorisation of the training set. In contrast, the MCC value is only 0.62 for the test set and considerably lower (0.49) for the out-of-sample set. These values primarily reflect a low precision where only 71% and 59% of the positive predictions are actually correct.