Molecular orbital energies prediction with ML algorithms
As several comments have mentioned, interpreting a machine learning method can often be difficult. Outside the question of machine learning, I think your question centers around "what would I want to know to roughly predict HOMO / LUMO energies?"

Unfortunately QM9 isn't very diverse chemically, but if we think from a general qualitative molecular orbital picture (e.g., building up diatomic molecules), you want to know:


Number of valence electrons (i.e., which orbitals are filled)
Atom type (which tells you valence electrons and atomic orbital energies)
Type of bond (e.g., single sigma bonds clearly will yield different MO energies than pi bonds)
Conjugation - conjugated and aromatic systems will have different MO energies than plain sigma-bonded species
Bond distances (from the Coulomb repulsion which encodes a distance $1/r$)


Given the above information, it's fairly easy to sketch out MO diagrams for $\ce{O2}$ or $\ce{HOH}$ so one can imagine that they're important for a machine learning algorithm too.

Now how do you know if they actually matter to the ML model?

One easy way is to drop one of the descriptors and retrain the model. Does it get worse? How much?

There are statistical techniques to give you some insight as well. For example, one can use LASSO to drop factors from the input representation that don't have much effect. You can also look at simple statistical correlation between input factors. I'd guess that "Atom Type" and "Atomic Number" are probably highly correlated with each other and only one is truly necessary.. but that's just a guess.