  Nonparametric regression models have recently surged in their power and
popularity, accompanying the trend of increasing dataset size and complexity.
While these models have proven their predictive ability in empirical settings,
they are often difficult to interpret and do not address the underlying
inferential goals of the analyst or decision maker. In this paper, we propose a
modular two-stage approach for creating parsimonious, interpretable summaries
of complex models which allow freedom in the choice of modeling technique and
the inferential target. In the first stage a flexible model is fit which is
believed to be as accurate as possible. In the second stage, lower-dimensional
summaries are constructed by projecting draws from the distribution onto
simpler structures. These summaries naturally come with valid Bayesian
uncertainty estimates. Further, since we use the data only once to move from
prior to posterior, these uncertainty estimates remain valid across multiple
summaries and after iteratively refining a summary. We apply our method and
demonstrate its strengths across a range of simulated and real datasets. Code
to reproduce the examples shown is avaiable at github.com/spencerwoody/ghost
