Explanation Tables.

We present a robust solution to the following problem: given a table with multiple categorical dimension attributes and one binary outcome attribute, construct a summary that offers an interpretable explanation of the factors affecting the outcome attribute in terms of the dimension attribute value combinations. We refer to such a summary as an explanation table, which is a disjunction of overlapping patterns over the dimension attributes, where each pattern specifies a conjunction of attribute=value conditions. The Flashlight algorithm that we describe is based on sampling and includes optimizations related to computing the information content of a summary from a sample of the data. Using real data sets, we demonstrate the advantages of explanation tables compared to related approaches that can be adapted to solve our problem, and we show significant performance benefits of our approach.