  We examine several aspects of explicability of a classification system built
from neural networks. The first aspect is the pairwise explicability, which is
the ability to provide the most accurate prediction when the range of
possibilities is narrowed to just two. Next we consider explicability in
development, which means ability to make incremental improvement in prediction
accuracy based on observed deficiency of the system. Inherent stochasticity of
neural network based classifiers can be interpreted using likelihood randomness
explicability. Finally, sureness explicability indicates confidence of the
classifying system to make any prediction at all.
  These concepts are examined in the framework of pairwise coupling, which is a
non-trainable metamodel that originated during development of support vector
machines. Several methodologies are evaluated, of which the key one is shown to
be the choice of the pairwise coupling method. We compare two methods: the
established Wu-Lin-Weng method with the recently proposed Bayes covariant
method. Our experiments indicate that the Wu-Lin-Weng method gives more weight
to a single pairwise classifier, whereas the latter tries to balance
information from the whole matrix of pairwise likelihoods. This translates into
higher accuracy, and better sureness predictions for the Bayes covariant
method.
  Pairwise coupling methodology has its costs, especially in terms of the
number of parameters (but not necessarily in terms of training costs). However,
when additional explicability aspects beyond accuracy are desired in an
application, the pairwise coupling models are a promising alternative to the
established methodology.
