Data planing allows one to determine which high-level variables play an important role in the network classification. In this procedure, one starts with the initial distribution (in arbitrary units) of a particular variable, in this example, the rotational velocity v_ϕ (top left), and then suppresses the information by weighting each star inversely by the probability that it falls within a particular histogram bin (top right). The planing procedure effectively removes the v_ϕ information from the dataset, but also affects the distribution of other variables. For example, the bottom row shows the proper motion in the right ascension, μ_α, distributions before and after planing in v_ϕ. By running the networks on the planed datasets, one can estimate the importance of the planed variable in driving the classification. The distributions in this figure pertain to the subset of m12i LSR0 with small parallax errors and a measurement of v_los.