Applications of machine learning tools to problems of physical interest are
often criticized for producing sensitivity at the expense of transparency. To
address this concern, we explore a data planing procedure for identifying
combinations of variables -- aided by physical intuition -- that can
discriminate signal from background. Weights are introduced to smooth away the
features in a given variable(s). New networks are then trained on this modified
data. Observed decreases in sensitivity diagnose the variable's discriminating
power. Planing also allows the investigation of the linear versus non-linear
nature of the boundaries between signal and background. We demonstrate the
efficacy of this approach using a toy example, followed by an application to an
idealized heavy resonance scenario at the Large Hadron Collider. By unpacking
the information being utilized by these algorithms, this method puts in context
what it means for a machine to learn.Comment: 6 pages, 3 figures. Version published in PRD, discussion adde