  A dataset has been classified by some unknown classifier into two types of
points. What were the most important factors in determining the classification
outcome? In this work, we employ an axiomatic approach in order to uniquely
characterize an influence measure: a function that, given a set of classified
points, outputs a value for each feature corresponding to its influence in
determining the classification outcome. We show that our influence measure
takes on an intuitive form when the unknown classifier is linear. Finally, we
employ our influence measure in order to analyze the effects of user profiling
on Google's online display advertising.
