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arXiv · 2312.04273

Invariant Random Forest: Tree-Based Model Solution for OOD Generalization

Yufan Liao·Qi Wu·Xing Yan
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Abstract

Out-Of-Distribution (OOD) generalization is an essential topic in machine learning. However, recent research is only focusing on the corresponding methods for neural networks. This paper introduces a novel and effective solution for OOD generalization of decision tree models, named Invariant Decision Tree (IDT). IDT enforces a penalty term with regard to the unstable/varying behavior of a split across different environments during the growth of the tree. Its ensemble version, the Invariant Random Forest (IRF), is constructed. Our proposed method is motivated by a theoretical result under mild conditions, and validated by numerical tests with both synthetic and real datasets. The superior performance compared to non-OOD tree models implies that considering OOD generalization for tree models is absolutely necessary and should be given more attention.

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Yufan Liao, Qi Wu, Xing Yan. 2023-12-07. Invariant Random Forest: Tree-Based Model Solution for OOD Generalization. https://arxiv.org/abs/2312.04273

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