  Predictive modeling applications increasingly use data representing people's
behavior, opinions, and interactions. Fine-grained behavior data often has
different structure from traditional data, being very high-dimensional and
sparse. Models built from these data are quite difficult to interpret, since
they contain many thousands or even many millions of features. Listing features
with large model coefficients is not sufficient, because the model coefficients
do not incorporate information on feature presence, which is key when analysing
sparse data. In this paper we introduce two alternatives for explaining
predictive models by listing important features. We evaluate these alternatives
in terms of explanation "bang for the buck,", i.e., how many examples'
inferences are explained for a given number of features listed. The bottom
line: (i) The proposed alternatives have double the bang-for-the-buck as
compared to just listing the high-coefficient features, and (ii) interestingly,
although they come from different sources and motivations, the two new
alternatives provide strikingly similar rankings of important features.
