Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models
arXiv:2605.23422v1 Announce Type: new Abstract: Learning high-quality oblique decision trees remains a significant challenge due to the discrete and non-convex nature of split optimization. We present the Hinge Regression Tree (HRT) framework, which reframes each oblique split as a nonlinear least-squares problem over two linear predictors whose max/min envelope induces ReLU-like representation capacity. We show that the resulting node-level optimization can be interpreted as a damped Newton method, and we establish the monotonic decrease of the node objective for its backtracking line-search
The continuous drive for more efficient and robust machine learning models necessitates novel approaches to fundamental algorithms like decision trees, especially as their application expands across complex datasets.
This development offers a significant improvement in the interpretability and performance of decision tree models, enhancing their utility in critical applications where accuracy and explainability are paramount.
The ability to construct higher-quality oblique decision trees through Newton-optimized methods means more compact and powerful tabular models, potentially expanding enterprise adoption and performance benchmarks.
- · AI/ML developers
- · Data scientists
- · Analytics software providers
- · Industries relying on tabular data modeling
- · Traditional decision tree algorithm users
- · Less efficient ML model providers
Improved predictive accuracy and model interpretability in machine learning applications across various sectors.
Accelerated adoption of advanced decision tree techniques in financial modeling, healthcare diagnostics, and other sensitive domains.
Increased demand for computational resources optimized for nonlinear least-squares and Newton-method calculations in ML infrastructure.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG