  Predictions of nuclear models guide the design of nuclear facilities to
ensure their safe and efficient operation. Because nuclear models often do not
perfectly reproduce available experimental data, decisions based on their
predictions may not be optimal. Awareness about systematic deviations between
models and experimental data helps to alleviate this problem. This paper shows
how a sparse approximation to Gaussian processes can be used to estimate the
model bias over the complete nuclide chart at the example of inclusive
double-differential neutron spectra for incident protons above 100\,MeV. A
powerful feature of the presented approach is the ability to predict the model
bias for energies, angles, and isotopes where data are missing. The number of
experimental data points that can be taken into account is at least in the
order of magnitude of~$10^4$ thanks to the sparse approximation. The approach
is applied to the Li\`ege Intranuclear Cascade Model (INCL) coupled to the
evaporation code ABLA. The results suggest that sparse Gaussian process
regression is a viable candidate to perform global and quantitative assessments
of models. Limitations of a philosophical nature of this (and any other)
approach are also discussed.
