Can a Single Tree Outperform an Entire Forest?
Keywords: Differentiable decision tree, Oblique decision tree, Subtree-polish strategy, Gradient-based optimization
Abstract: The prevailing mindset is that a single decision tree underperforms random forests in testing accuracy, despite its advantages in interpretability and lightweight structure. This study challenges such a mindset by significantly improving the testing accuracy of an oblique regression tree through our gradient-based entire tree optimization framework, making its performance comparable to random forests. Our approach reformulates tree training as a differentiable unconstrained optimization task, employing a scaled sigmoid approximation strategy. To ameliorate numerical instability, we propose an algorithmic scheme that solves a sequence of increasingly accurate approximations. Additionally, a subtree polish strategy is implemented to reduce approximation errors accumulated across the tree. Extensive experiments on 16 datasets demonstrate that our optimized tree outperforms random forests by an average of 2.03\% improvements in testing accuracy.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 8371
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