  Machine-learning classifiers can be leveraged as a two-sample statistical
test. Suppose each sample is assigned a different label and that a classifier
can obtain a better-than-chance result discriminating them. In this case, we
can infer that both samples originate from different populations. However, many
types of models, such as neural networks, behave as a black-box for the user:
they can reject that both samples originate from the same population, but they
do not offer insight into how both samples differ. Self-Organizing Maps are a
dimensionality reduction initially devised as a data visualization tool that
displays emergent properties, being also useful for classification tasks. Since
they can be used as classifiers, they can be used also as a two-sample
statistical test. But since their original purpose is visualization, they can
also offer insights.
