  Accurate approximations to density functionals have recently been obtained
via machine learning (ML). By applying ML to a simple function of one variable
without any random sampling, we extract the qualitative dependence of errors on
hyperparameters. We find universal features of the behavior in extreme limits,
including both very small and very large length scales, and the noise-free
limit. We show how such features arise in ML models of density functionals.
