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 the 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.