Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach
This work provides an incremental improvement for researchers and practitioners using hyperbolic machine learning by making tree-based models more efficient and easier to deploy.
The paper tackles the problem of extending decision trees to hyperbolic spaces by reformulating the hyperDT algorithm in the Beltrami-Klein model, enabling a wrapper approach that improves speed and accuracy while simplifying implementation.
Decision trees and models that use them as primitives are workhorses of machine learning in Euclidean spaces. Recent work has further extended these models to the Lorentz model of hyperbolic space by replacing axis-parallel hyperplanes with homogeneous hyperplanes when partitioning the input space. In this paper, we show how the hyperDT algorithm can be elegantly reexpressed in the Beltrami-Klein model of hyperbolic spaces. This preserves the thresholding operation used in Euclidean decision trees, enabling us to further rewrite hyperDT as simple pre- and post-processing steps that form a wrapper around existing tree-based models designed for Euclidean spaces. The wrapper approach unlocks many optimizations already available in Euclidean space models, improving flexibility, speed, and accuracy while offering a simpler, more maintainable, and extensible codebase. Our implementation is available at https://github.com/pchlenski/hyperdt.