  High-resolution cosmological hydrodynamic simulations are currently limited
to relatively small volumes due to their computational expense. However, much
larger volumes are required to probe rare, overdense environments, and measure
clustering statistics of the large scale structure. Typically, zoom simulations
of individual regions are used to study rare environments, and semi-analytic
models and halo occupation models applied to dark matter only (DMO) simulations
are used to study the Universe in the large-volume regime. We propose a new
approach, using a machine learning framework to explore the halo-galaxy
relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of
galaxy clusters. We train a tree based machine learning method to predict the
baryonic properties of galaxies based on their host dark matter halo
properties. The trained model successfully reproduces a number of key
distribution functions for an infinitesimal fraction of the computational cost
of a full hydrodynamic simulation. By training on both periodic simulations as
well as zooms of overdense environments, we learn the bias of galaxy evolution
in differing environments. This allows us to apply the trained model to a
larger DMO volume than would be possible if we only trained on a periodic
simulation. We demonstrate this application using the $(800 \; \mathrm{Mpc})^3$
P-Millennium simulation, and present predictions for key baryonic distribution
functions and clustering statistics from the EAGLE model in this large volume.
