Random Hinge Forest for Differentiable Learning

Nathan Lay, Adam P. Harrison, Sharon Schreiber +2

We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component wi…