  Mass loss is a key property to understand stellar evolution and in particular
for low-metallicity environments. Our knowledge has improved dramatically over
the last decades both for single and binary evolutionary models. However,
episodic mass loss although definitely present observationally, is not included
in the models, while its role is currently undetermined. A major hindrance is
the lack of large enough samples of classified stars. We attempted to address
this by applying an ensemble machine-learning approach using color indices
(from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining
the probabilities from three different algorithms. We trained on M31 and M33
sources with known spectral classification, which we grouped into
Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet
and background galaxies/AGNs. We then applied the classifier to about one
million Spitzer point sources from 25 nearby galaxies, spanning a range of
metallicites ($1/15$ to $\sim3~Z_{\odot}$). Equipped with spectral
classifications we investigated the occurrence of these populations with
metallicity.
