从多个表现相近的模型中精选组合，提升预测鲁棒性。
"A 6 or a 9?": Ensemble Learning Through the Multiplicity of Performant Models and Explanations
- 基于性能与解释相似性分组，挑选多样化高表现模型
- 在高多样性场景下，最高提升0.20+ AUROC
- 适合需要稳定预测的医疗、制造等真实业务场景
从历史数据构建模型并在新数据上保持有效，是机器学习的核心。然而，选择具有良好泛化能力的模型仍具挑战性。当存在多个表现相近的模型时，即出现Rashomon效应，这在制造过程或医疗诊断等真实场景中常见。本文提出Rashomon Ensemble方法，从这些多样化的高性能模型中进行策略性选择，以增强泛化能力。通过结合模型性能与解释的一致性进行分组，构建的集成模型在保持预测准确的同时最大化多样性，使每个模型覆盖解空间的不同区域，从而更抗分布偏移和未见数据变化。我们在公开及专有协作的真实数据集上验证该方法，在Rashomon比率较大的情况下，最多实现0.20+ AUROC提升。此外，实证显示该方法在多种实际应用中为商业带来显著效益，体现出其鲁棒性、实用性与高效性。
原文摘要 · Abstract (English)
Creating models from past observations and ensuring their effectiveness on new data is the essence of machine learning. However, selecting models that generalize well remains a challenging task. Related to this topic, the Rashomon Effect refers to cases where multiple models perform similarly well for a given learning problem. This often occurs in real-world scenarios, like the manufacturing process or medical diagnosis, where diverse patterns in data lead to multiple high-performing solutions. We propose the Rashomon Ensemble, a method that strategically selects models from these diverse high-performing solutions to improve generalization. By grouping models based on both their performance and explanations, we construct ensembles that maximize diversity while maintaining predictive accuracy. This selection ensures that each model covers a distinct region of the solution space, making the ensemble more robust to distribution shifts and variations in unseen data. We validate our approach on both open and proprietary collaborative real-world datasets, demonstrating up to 0.20+ AUROC improvements in scenarios where the Rashomon ratio is large. Additionally, we demonstrate tangible benefits for businesses in various real-world applications, highlighting the robustness, practicality, and effectiveness of our approach.
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