  MSSM-like string models from the compactification of the heterotic string on
toroidal orbifolds (of the kind $T^6/P$) have distinct phenomenological
properties, like the spectrum of vector-like exotics, the scale of
supersymmetry breaking, and the existence of non-Abelian flavor symmetries. We
show that these characteristics depend crucially on the choice of the
underlying orbifold point group $P$. In detail, we use boosted decision trees
to predict $P$ from phenomenological properties of MSSM-like orbifold models.
As this works astonishingly well, we can utilize machine learning to predict
the orbifold origin of the MSSM.
