  One emerging application of machine learning methods is the inference of
galaxy cluster masses. In this note, machine learning is used to directly
combine five simulated multiwavelength measurements in order to find cluster
masses. This is in contrast to finding mass estimates for each observable,
normally by using a scaling relation, and then combining these scaling law
based mass estimates using a likelihood. We also illustrate how the
contributions of each observable to the accuracy of the resulting mass
measurement can be compared via model-agnostic Importance Permutation values.
Thirdly, as machine learning relies upon the accuracy of the training set in
capturing observables, their correlations, and the observational selection
function, and as the machine learning training set originates from simulations,
two tests of whether a simulation's correlations are consistent with
observations are suggested and explored as well.
