  Stellar streams formed by tidal stripping of progenitors orbiting around the
Milky Way are expected to be perturbed by encounters with dark matter subhalos.
Recent studies have shown that they are an excellent proxy to infer properties
of the perturbers, such as their mass. Here we present two different
methodologies that make use of the fully non-Gaussian density distribution of
stellar streams: a Bayesian model selection based on the probability density
function (PDF) of stellar density, and a likelihood-free gradient boosting
classifier. As an application, we forecast model selection strength of evidence
for cold dark matter clusters of masses $10^3$-$10^5 M_{\odot}$, $10^5$-$10^7
M_{\odot}$ and $10^7$-$10^9 M_{\odot}$, based on a GD-1-like stellar stream and
including realistic observational errors. Evidence for the smaller mass range,
so far under-explored, is particularly interesting for the primordial black
holes cold dark matter hypothesis. We expect moderate to strong evidence for
model selection based on the PDF analysis when assuming low and intermediate
dark matter perturbers mass ranges as fiducial models, but only weak evidence
when the larger mass range is taken to be the fiducial model. Instead, the
gradient boosting model is a highly efficient classifier ($F_1$-scores larger
than 90%) for all mass ranges here considered.
