  This paper shows that gradient boosting based on symmetric decision trees can
be equivalently reformulated as a kernel method that converges to the solution
of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence
to a Gaussian Process' posterior mean, which, in turn, allows us to easily
transform gradient boosting into a sampler from the posterior to provide better
knowledge uncertainty estimates through Monte-Carlo estimation of the posterior
variance. We show that the proposed sampler allows for better knowledge
uncertainty estimates leading to improved out-of-domain detection.
