Unseen test words are mapped to one <UNK> vector, sampled from the the space of trained word vectors relying on a normal distribution and the same fixed random seed for all models.
kN5 by design has a slightly different way of handling unseen test words: they are regarded as outliers and assigned low-probability estimates.