  In 2001, Leo Breiman wrote of a divide between "data modeling" and
"algorithmic modeling" cultures. Twenty years later this division feels far
more ephemeral, both in terms of assigning individuals to camps, and in terms
of intellectual boundaries. We argue that this is largely due to the "data
modelers" incorporating algorithmic methods into their toolbox, particularly
driven by recent developments in the statistical understanding of Breiman's own
Random Forest methods. While this can be simplistically described as "Breiman
won", these same developments also expose the limitations of the
prediction-first philosophy that he espoused, making careful statistical
analysis all the more important. This paper outlines these exciting recent
developments in the random forest literature which, in our view, occurred as a
result of a necessary blending of the two ways of thinking Breiman originally
described. We also ask what areas statistics and statisticians might currently
overlook.
