  In Random Forests, proximity distances are a metric representation of data
into decision space. By observing how changes in input map to the movement of
instances in this space we are able to determine the independent contribution
of each feature to the decision-making process. For binary feature vectors,
this process is fully specified. As these changes in input move particular
instances nearer to the in-group or out-group, the independent contribution of
each feature can be uncovered. Using this technique, we are able to calculate
the contribution of each feature in determining how black-box decisions were
made. This allows explication of the decision-making process, audit of the
classifier, and post-hoc analysis of errors in classification.
