研究团队提出把梯度提升集成叶值当坐标的新方法，能让模型决策解释更精准，复现决策和实际可行性都比普通方法好
梯度提升集成通过将叶值视为坐标而非中间结果，使实例成为高维空间中的点并实现线性作用；这种视角改变让对比性解释更精确，实例差异向量在共享叶处为零，被拒与被接受的差距由可追溯的树分裂决定；研究团队在五组表格数据集上构建回溯方法，其建议复现模型决策精度达6.2×10^-15，且在实际可行改变下有效性保留58%高于基线的41%
Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles
A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as coordinates rather than as intermediate results, and every instance becomes a point in R^M on which the model acts linearly: the score is the sum of the coordinates. This small change of view makes contrastive explanation exact. The difference between two instances is a vector that is identically zero wherever they share a leaf, so the gap between a rejected applicant and an accepted one is carried by a handful of coordinates, each traceable to a real split in a real tree. Nothing is fitted, sampled, or assumed additive in features -- the additivity is already there, in the right space. We build a recourse method on this representation and evaluate it on five tabular datasets under repeated cross-validation. Its recommendation reconstructs the model's own decision to 6.2 x 10^-15, so an auditor can re-check the arithmetic without the model. On the credit datasets it is Pareto-non-dominated on effort against realism. And when recommendations are restricted to changes the subject could actually make -- not their age, not a settled delinquency -- it retains 58% of its validity where the strongest baseline retains 41%, a distinction the standard evaluation cannot see because it never asks whether a recommendation can be carried out.