  Random forest methods belong to the class of non-parametric machine learning
algorithms. They were first introduced in 2001 by Breiman and they perform with
accuracy in high dimensional settings. In this article, we consider, a
simplified kernel-based random forest algorithm called simplified directional
KeRF (Kernel Random Forest). We establish the asymptotic equivalence between
simplified directional KeRF and centered KeRF, with additional numerical
experiments supporting our theoretical results.
