@article{6670,
  title = {How to Explain Individual Classification Decisions},
  journal = {Journal of Machine Learning Research},
  abstract = {After building a classifier with modern tools of machine learning we typically have a black box at
  hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the
  most likely label of a given unseen data point. However, most methods will provide no answer why
  the model predicted a particular label for a single instance and what features were most influential
  for that particular instance. The only method that is currently able to provide such explanations are
  decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to
  explain the decisions of any classification method.},
  volume = {11},
  pages = {1803-1831},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2010},
  slug = {6670},
  author = {Baehrens, D. and Schroeter, T. and Harmeling, S. and Kawanabe, M. and Hansen, K. and M{\"u}ller, K-R.},
  month_numeric = {6}
}