  Machine Learning (ML) provides important techniques for classification and
predictions. Most of these are black-box models for users and do not provide
decision-makers with an explanation. For the sake of transparency or more
validity of decisions, the need to develop explainable/interpretable ML-methods
is gaining more and more importance. Certain questions need to be addressed:
  How does an ML procedure derive the class for a particular entity? Why does a
particular clustering emerge from a particular unsupervised ML procedure? What
can we do if the number of attributes is very large? What are the possible
reasons for the mistakes for concrete cases and models?
  For binary attributes, Formal Concept Analysis (FCA) offers techniques in
terms of intents of formal concepts, and thus provides plausible reasons for
model prediction. However, from the interpretable machine learning viewpoint,
we still need to provide decision-makers with the importance of individual
attributes to the classification of a particular object, which may facilitate
explanations by experts in various domains with high-cost errors like medicine
or finance.
  We discuss how notions from cooperative game theory can be used to assess the
contribution of individual attributes in classification and clustering
processes in concept-based machine learning. To address the 3rd question, we
present some ideas on how to reduce the number of attributes using similarities
in large contexts.
