Tree-based ensembles such as random forests remain the go-to for tabular data over deep learning models due to their prediction performance and computational efficiency. These advantages have led to their widespread deployment in high-stakes domains, where interpretability is essential for ensuring trustworthy predictions. This has motivated the development of popular local feature importance methods such as LIME and TreeSHAP. However, these approaches rely on approximations that ignore the model's internal structure and instead depend on potentially unstable perturbations. These issues are addressed in the global setting by MDI+, a global feature importance method which combines tree-based and linear feature importances by exploiting an equivalence between decision trees and least squares on a transformed node basis. However, the global MDI+ scores are not able to explain predictions when faced with heterogeneous individual characteristics. To address this gap, we propose Local MDI+ (LMDI+), a novel extension of the MDI+ framework that quantifies feature importances for each particular sample. Across twelve real-world benchmark datasets, LMDI+ outperforms existing baselines at identifying instance-specific predictive features, yielding an average 10% improvement in predictive performance when using only the selected features. It further demonstrates greater stability by consistently producing similar instance-level feature importance rankings across repeated model fits with different random seeds. Ablation experiments show that each component of LMDI+ contributes to these gains, and that the improvements extend beyond random forests to gradient boosting models. Finally, we show that LMDI+ enables local interpretability use cases by identifying closely matched counterfactuals for each classification benchmark and discovering homogeneous subgroups in a housing dataset case study.

翻译：树基集成方法（如随机森林）因其预测性能和计算效率，在表格数据任务中仍优于深度学习模型。这些优势使其被广泛部署于高风险领域，而可解释性对确保预测可信度至关重要。这推动了LIME和TreeSHAP等主流局部特征重要性方法的发展。然而，这些方法依赖忽略模型内部结构的近似手段，转而使用可能不稳定的扰动机制。全局MDI+方法通过利用决策树与最小二乘法在转换节点基上的等价性，结合树基与线性特征重要性，解决了上述问题。但全局MDI+评分无法解释存在异质性个体特征时的预测。为弥补这一空白，我们提出局部MDI+（LMDI+）——MDI+框架的新扩展，可量化每个样本的特征重要性。在12个真实世界基准数据集上，LMDI+在识别实例特异性预测特征方面优于现有基线，仅使用所选特征即可使预测性能平均提升10%。该方法在不同随机种子的重复模型拟合中持续生成相似的实例级特征重要性排序，展现出更强稳定性。消融实验表明，LMDI+的每个组件均贡献了性能增益，且改进范围从随机森林扩展至梯度提升模型。最后，我们通过为每个分类基准识别高度匹配的反事实样本，并在住房数据集案例研究中发现同质子群，验证了LMDI+支持局部可解释性应用场景的能力。