  To fully harvest the rich library of stellar elemental abundance data
available, we require reliable models that facilitate our interpretation of
them. Galactic chemical evolution (GCE) models are one such set, and a key part
of which are the selection of chemical yields from different nucleosynthetic
enrichment channels, predominantly asymptotic giant branch (AGB) stars, Type Ia
supernovae (SNe Ia), and core-collapse supernovae (CC-SNe). Here, we present a
scoring system for yield tables based on their ability to reproduce proto-solar
abundances within a simple parametrisation of the GCE modelling software
Chempy, which marginalises over galactic parameters describing simple stellar
populations (SSPs) and interstellar medium physics. Two statistical scoring
methods are presented, based on Bayesian evidence and leave-one-out
cross-validation and are applied to five CC-SN tables, (a) for all mutually
available elements and (b) for a subset of the 9 most abundant elements. We
find that the yields of Prantzos et al. (P18, including stellar rotation) and
Chieffi & Limongi (C04) best reproduce proto-solar abundances for the two
cases, respectively. The inferred best-fit SSP parameters for (b) are
$\alpha_\mathrm{IMF}=-2.45^{+0.15}_{-0.11}$ for the initial mass function
high-mass slope and $\mathrm{N}_\mathrm{Ia}=1.29^{+0.45}_{-0.31}\times10^{-3}$
M$_\odot^{-1}$ for the SN Ia normalisation, which are broadly consistent across
tested yield tables. Additionally, we demonstrate how Chempy can be used to
dramatically improve elemental abundance predictions of hydrodynamical
simulations by plugging tailored best-fit SSP parameters into a Milky Way
analogue from Gutcke & Springel. Our code, including a comprehensive tutorial,
is freely available and can additionally provide SSP enrichment tables for any
combination of parameters and yield tables.
