Background: Understanding individual predictions in complex tabular models remains challenging, as existing additive frameworks like SHAP and LIME often obscure structural logic by absorbing nonlinear...
The paper introduces localized case-based feature importance (CBFI), a framework that decomposes individual predictions in complex tabular models into distinct structural components, separating independent main effects from pure interactions. Unlike additive explanations such as SHAP and LIME, CBFI explicitly captures feature synergies, enabling the recovery of latent interaction structures. Experiments on wine quality classification and medical insurance regression, along with fidelity tests and synthetic benchmarks, show that CBFI accurately disentangles interactions hidden by additive models and aligns with the nonlinear dependencies in gradient boosting and deep neural networks.
By Sejong Oh
The paper proposes a new evaluation test for explanation methods: if an explanation accurately captures how a model uses its features, one should be able to reconstruct the model’s predictions from it. The authors convert explanations into predictors by summing feature effects and assess how well these predictors reproduce the model on unseen data, without any fitting. They apply this test to partial dependence plots, accumulated local effects, SHAP, and LIME across multiple datasets and model families, showing that the best method depends on feature dependence and that some existing quality metrics can favor flawed explanations.
By Jacob Selb{\ae}k, Hugo L. Hammer
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables.
arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.
By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu
arXiv:2603.02221v3 Announce Type: replace-cross
Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasi...
By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu