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

• URL: http://arxiv.org/abs/2312.04273v3
• Date: Thu, 18 Jan 2024 01:52:47 GMT
• Title: Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
• Authors: Yufan Liao, Qi Wu, Xing Yan
• Abstract summary: 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. Our proposed method is motivated by a theoretical result under mild conditions, and validated by numerical tests with both synthetic and real datasets.
• Score: 13.259844672078552
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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