提出新方法让更新决策树时解释结果不变，避免误导用户。
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
- 设计可衡量解释变化的损失函数BCLTX，抑制模型更新导致的解释变动。
- 改进CART算法，使更新后预测性能与解释稳定性兼顾，10个数据集验证有效。
- 适合需要稳定解释的医疗、金融等高风险决策场景使用。
在机器学习模型运行中，模型更新是基础流程，但需谨慎评估其对下游决策的影响。尤其在可解释模型中，模型更新引发的解释变化可能对用户造成负面影响。决策树因高度透明，常用于高风险决策场景，此类问题尤为突出。然而，现有研究多聚焦于特征贡献类解释，无法处理树结构本身的解释变化。为此，本文提出树结构解释的后向兼容性损失（BCLTX），用以抑制模型更新前后决策树解释的变化。进一步设计了具备后向兼容性的改进型CART算法（CART-BCTX），在保持预测性能的同时增强解释稳定性。在10个真实世界数据集（含分类与回归任务）上的实验表明，CART-BCTX在预测性能与BCLTX值之间取得良好平衡，计算时间与原始CART相当，适用于各类任务。
原文摘要 · Abstract (English)
In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decision-making. Particularly when operating explainable models, changes in explanations resulting from model updates can lead to detrimental outcomes for users. Decision trees, due to their high transparency, are frequently employed in risk-sensitive decision-making and serve as a prominent example in which the aforementioned issue is evident. However, existing research addressing similar issues has focused on explanations based on feature contributions, and thus cannot handle explanations derived from tree structures. Therefore, this paper proposes the Backward Compatibility Loss in Tree-based eXplanations (BCLTX), a loss metric that suppresses changes in decision tree explanations before and after updates. Furthermore, we design CART with Backward Compatibility in Tree-based eXplanations (CART-BCTX), a lightweight algorithm that improves upon CART for the decision tree update problem under BCLTX. Experimental results using 10 real-world datasets, including both classification and regression tasks, show that CART-BCTX achieves favorable trade-offs between prediction performances and BCLTX values, with comparable computation times to CART, regardless of the task.
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