  We address the degree to which machine learning can be used to accurately and
transferably predict post-Hartree-Fock correlation energies. Refined strategies
for feature design and selection are presented, and the molecular-orbital-based
machine learning (MOB-ML) method is applied to several test systems.
Strikingly, for the MP2, CCSD, and CCSD(T) levels of theory, it is shown that
the thermally accessible (350 K) potential energy surface for a single water
molecule can be described to within 1 millihartree using a model that is
trained from only a single reference calculation at a randomized geometry. To
explore the breadth of chemical diversity that can be described, MOB-ML is also
applied to a new dataset of thermalized (350 K) geometries of 7211 organic
models with up to seven heavy atoms. In comparison with the previously reported
$\Delta$-ML method, MOB-ML is shown to reach chemical accuracy with three-fold
fewer training geometries. Finally, a transferability test in which models
trained for seven-heavy-atom systems are used to predict energies for
thirteen-heavy-atom systems reveals that MOB-ML reaches chemical accuracy with
36-fold fewer training calculations than $\Delta$-ML (140 versus 5000 training
calculations).
