  Trans-Neptunian objects (TNOs) are small, icy bodies in the outer solar
system. They are observed to have a complex orbital distribution that was
shaped by the early dynamical history and migration of the giant planets.
Comparisons between the different dynamical classes of modeled and observed
TNOs can help constrain the history of the outer solar system. Because of the
complex dynamics of TNOs, particularly those in and near mean motion resonances
with Neptune, classification has traditionally been done by human inspection of
plots of the time evolution of orbital parameters. This is very inefficient.
The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected
to increase the number of known TNOs by a factor of $\sim$10, necessitating a
much more automated process. In this chapter we present an improved supervised
machine learning classifier for TNOs. Using a large and diverse training set as
well as carefully chosen, dynamically motivated data features calculated from
numerical integrations of TNO orbits, our classifier returns results that match
those of a human classifier 98% of the time, and dynamically relevant
classifications 99.7% of the time. This classifier is dramatically more
efficient than human classification, and it will improve classification of both
observed and modeled TNO data.
