  In the outer solar system, the Kuiper Belt contains dynamical sub-populations
sculpted by a combination of planet formation and migration and gravitational
perturbations from the present-day giant planet configuration. The subdivision
of observed Kuiper Belt objects (KBOs) into different dynamical classes is
based on their current orbital evolution in numerical integrations of their
orbits. Here we demonstrate that machine learning algorithms are a promising
tool for reducing both the computational time and human effort required for
this classification. Using a Gradient Boosting Classifier, a type of machine
learning regression tree classifier trained on features derived from short
numerical simulations, we sort observed KBOs into four broad, dynamically
distinct populations - classical, resonant, detached, and scattering - with a
>97 per cent accuracy for the testing set of 542 securely classified KBOs. Over
80 per cent of these objects have a $>3\sigma$ probability of class membership,
indicating that the machine learning method is classifying based on the
fundamental dynamical features of each population. We also demonstrate how, by
using computational savings over traditional methods, we can quickly derive a
distribution of class membership by examining an ensemble of object clones
drawn from the observational errors. We find two major reasons for
misclassification: inherent ambiguity in the orbit of the object - for
instance, an object that is on the edge of resonance - and a lack of
representative examples in the training set. This work provides a promising
avenue to explore for fast and accurate classification of the thousands of new
KBOs expected to be found by surveys in the coming decade.
