  Due to the increasing use of machine learning in practice it becomes more and
more important to be able to explain the prediction and behavior of machine
learning models. An instance of explanations are counterfactual explanations
which provide an intuitive and useful explanations of machine learning models.
In this survey we review model-specific methods for efficiently computing
counterfactual explanations of many different machine learning models and
propose methods for models that have not been considered in literature so far.
