  Elucidating the connection between the properties of galaxies and the
properties of their hosting haloes is a key element in galaxy formation. When
the spatial distribution of objects is also taken under consideration, it
becomes very relevant for cosmological measurements. In this paper, we use
machine learning techniques to analyse these intricate relations in the
IllustrisTNG300 magnetohydrodynamical simulation, predicting baryonic
properties from halo properties. We employ four different algorithms: extremely
randomized trees, K-nearest neighbours, light gradient boosting machine, and
neural networks, along with a unique and powerful combination of the results
from all four approaches. Overall, the different algorithms produce consistent
results in terms of predicting galaxy properties from a set of input halo
properties that include halo mass, concentration, spin, and halo overdensity.
For stellar mass, the Pearson correlation coefficient is 0.98, dropping down to
0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition,
we apply, for the first time in this context, an existing data augmentation
method, synthetic minority over-sampling technique for regression with Gaussian
noise (SMOGN), designed to alleviate the problem of imbalanced data sets,
showing that it improves the overall shape of the predicted distributions and
the scatter in the halo-galaxy relations. We also demonstrate that our
predictions are good enough to reproduce the power spectra of multiple galaxy
populations, defined in terms of stellar mass, sSFR, colour, and size with high
accuracy. Our results align with previous reports suggesting that certain
galaxy properties cannot be reproduced using halo features alone.
