  Interpretation of a machine learning induced models is critical for feature
engineering, debugging, and, arguably, compliance. Yet, best of breed machine
learning models tend to be very complex. This paper presents a method for model
interpretation which has the main benefit that the simple interpretations it
provides are always grounded in actual sets of learning examples. The method is
validated on the task of interpreting a complex regression model in the context
of both an academic problem -- predicting the year in which a song was recorded
and an industrial one -- predicting mail user churn.
