AI RESEARCH
Hinge Regression Tree: A Newton Method for Oblique Regression Tree Splitting
arXiv CS.LG
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ArXi:2602.05371v2 Announce Type: replace Oblique decision trees combine the transparency of trees with the power of multivariate decision boundaries, but learning high-quality oblique splits is NP-hard, and practical methods still rely on slow search or theory-free heuristics. We present the Hinge Regression Tree (HRT), which reframes each split as a non-linear least-squares problem over two linear predictors whose max/min envelope induces ReLU-like expressive power.