  In this paper we present a heuristic method to provide individual
explanations for those elements in a dataset (data points) which are wrongly
predicted by a given classifier. Since the general case is too difficult, in
the present work we focus on faulty data from an underfitted model. First, we
project the faulty data into a hand-crafted, and thus human readable,
intermediate representation (meta-representation, profile vectors), with the
aim of separating the two main causes of miss-classification: the classifier is
not strong enough, or the data point belongs to an area of the input space
where classes are not separable. Second, in the space of these profile vectors,
we present a method to fit a meta-classifier (decision tree) and express its
output as a set of interpretable (human readable) explanation rules, which
leads to several target diagnosis labels: data point is either correctly
classified, or faulty due to a too weak model, or faulty due to mixed
(overlapped) classes in the input space. Experimental results on several real
datasets show more than 80% diagnosis label accuracy and confirm that the
proposed intermediate representation allows to achieve a high degree of
invariance with respect to the classifier used in the input space and to the
dataset being classified, i.e. we can learn the metaclassifier on a dataset
with a given classifier and successfully predict diagnosis labels for a
different dataset or classifier (or both).
