  Using data from TNG300-2, we train a neural network (NN) to recreate the
stellar mass ($M^*$) and star formation rate (SFR) of central galaxies in a
dark-matter-only simulation. We consider 12 input properties from the halo and
sub-halo hosting the galaxy and the near environment. $M^*$ predictions are
robust, but the machine does not fully reproduce its scatter. The same happens
for SFR, but the predictions are not as good as for $M^*$. We chained neural
networks, improving the predictions on SFR to some extent. For SFR, we
time-averaged this value between $z=0$ and $z=0.1$, which improved results for
$z=0$. Predictions of both variables have trouble reproducing values at lower
and higher ends. We also study the impact of each input variable in the
performance of the predictions using a leave-one-covariate-out approach, which
led to insights about the physical and statistical relation between input
variables. In terms of metrics, our machine outperforms similar studies, but
the main discoveries in this work are not linked with the quality of the
predictions themselves, but to how the predictions relate to the input
variables. We find that previously studied relations between physical variables
are meaningful to the machine. We also find that some merger tree properties
strongly impact the performance of the machine. %We highlight the value of
machine learning (ML) methods in helping understand the information contained
in different variables, since with its help we were able to obtain useful
insights resulting from studying the impact of input variables on the resulting
behaviour of galaxy properties. We conclude that ML models are useful tools to
understand the significance of physical different properties and their impact
on target characteristics, as well as strong candidates for potential
simulation methods.
