  We use the output of a random forest to define a family of local smoothers
with spatially adaptive bandwidth matrices. The smoother inherits the
flexibility of the original forest but, since it is a simple, linear smoother,
it is very interpretable and it can be used for tasks that would be intractable
for the original forest. This includes bias correction, confidence intervals,
assessing variable importance and methods for exploring the structure of the
forest. We illustrate the method on some synthetic examples and on data related
to Covid-19.
