  The interplay of the chemistry and physics that exists within astrochemically
relevant sources can only be fully appreciated if we can gain a holistic
understanding of their chemical inventories. Previous work by Lee et al. (2021)
demonstrated the capabilities of simple regression models to reproduce the
abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1),
as well as provide abundance predictions for new candidate molecules. It
remains to be seen, however, to what degree TMC-1 is a ``unicorn'' in
astrochemistry, where the simplicity of its chemistry and physics readily
facilitates characterization with simple machine learning models. Here we
present an extension in chemical complexity to a heavily studied high-mass star
forming region: the Orion Kleinmann-Low (Orion KL) nebula. Unlike TMC-1, Orion
KL is composed of several structurally distinct environments that differ
chemically and kinematically, wherein the column densities of molecules between
these components can have non-linear correlations that cause the unexpected
appearance or even lack of likely species in various environments. This
proof-of-concept study used similar regression models sampled by Lee et al.
(2021) to accurately reproduce the column densities from the XCLASS fitting
program presented in Crockett et al. (2014).
