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Invariant Random Forest: Tree-Based Model Solution for OOD Generalization (2312.04273v3)

Published 7 Dec 2023 in cs.LG

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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Citations (2)
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Summary

  • The paper presents a novel tree-based ensemble that integrates invariant decision trees with a penalty term to prioritize stable features.
  • The approach outperforms traditional Random Forests and XGBoost by reducing variance and enhancing predictive reliability on unseen data.
  • The methodology offers practical benefits for real-world applications in finance and healthcare, where data shifts pose significant challenges.

Introduction

Machine learning models have achieved considerable success in various domains when the training and testing data distributions match. However, these models usually experience performance degradation when exposed to out-of-distribution (OOD) data they haven't seen during training—a scenario often encountered in real-world applications. This challenge has led to a search for models that can maintain their predictive performance even when tested on data that differs from the training set.

Tree-Based Models and OOD Generalization

Decision trees, known for their simplicity and interpretability, are widely used in domains that demand high reliability, such as healthcare and finance. Although decision trees provide clear and easily understandable decision paths, they too can struggle with OOD generalization just like deep neural networks (DNNs). This motivates a need for tree-based models that can effectively handle data distribution shifts and avoid over-relying on spurious correlations that may not hold outside the training set.

Invariant Decision Trees and Random Forests

The paper proposes a new approach to OOD generalization for decision tree models, named the Invariant Decision Tree (IDT). The IDT model introduces a penalty term to the tree growth process that encourages the use of certain stable features over others that may vary between environments. Building on the IDT, the Invariant Random Forest (IRF) is constructed as an ensemble method that maintains the benefits of the IDT while also enjoying the reduced variance that comes from averaging multiple decision trees. The theoretical motivation behind the approach demonstrates its effectiveness under mild assumptions, and the results are validated through numerical experiments.

Performance on Synthetic and Real Data

Testing on both synthetic data and real-world datasets, the IRF showcased better OOD generalization compared to traditional Random Forest (RF) and Gradient Boosting Decision Tree (XGBoost), particularly when adjusting the penalty term to emphasize stable features. Additionally, the framework allows for scenario-specific training, offering options when environmental data is either absent or fully accessible, which makes it versatile in different applied settings. Through these experiments, IRF was shown to favor stable variables during splitting, leading to better predictive performance in unseen environments.

Conclusion

The Invariant Random Forest represents a step forward in addressing OOD generalization in tree-based models. By leveraging stable feature selection during tree growth, the method helps reduce the use of variables that might cause instability in predictions when faced with new data distributions. This approach has practical implications for enhancing the reliability of machine learning models in real-life scenarios where distribution shifts are inevitable.

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Authors (3)

  1. Yufan Liao
  2. Qi Wu
  3. Xing Yan
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