arXiv:2604. 15107v2 Announce Type: replace-cross Abstract: Shapley values provide a flexible framework for attributing feature contributions to model predictions, but they are not naturally suited for feature selection: a feature may receive a positive attribution even when it is redundant given the remaining variables.
arXiv:2608. 11508v1 Announce Type: new Abstract: Machine learning pipelines commonly flatten relational data into single-table representations, discarding structural constraints.
arXiv:2511.15371v3 Announce Type: replace Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous m...
arXiv:2607. 24145v1 Announce Type: new Abstract: Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task.
arXiv:2610.01641v1 Announce Type: cross Abstract: Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive...
arXiv:2602. 09326v2 Announce Type: replace Abstract: Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable.
arXiv:2510.12734v2 Announce Type: replace Abstract: Variable importance (VI) methods are often used for hypothesis generation, feature selection, and scientific validation. In the standard VI pipelin...
The paper introduces a unified framework called null importance to clarify different notions of feature relevance in interpretable machine learning. It defines null importance at the population level for various relevance concepts—marginal, conditional, predictive risk, functional invariance, and causal effects—and demonstrates how each answers distinct scientific questions. Through theoretical analysis, simulations, and case studies on fairness and genomic modeling, the authors show when these null notions coincide or diverge and how different importance methods target them.
arXiv:2609.10313v1 Announce Type: cross Abstract: Shapley values are widely used for post-hoc feature attribution, but most estimators return point quantities and do not quantify uncertainty, and pop...
The paper introduces a hypothesis‑testing framework that embeds feature importance methods (FIMs) within a Weight of Evidence (WoE) analysis. By quantifying how strongly observed evidence supports a given hypothesis—whether from domain knowledge, ground truth, or the FIM itself—the approach evaluates FIM alignment and variability. The authors provide theoretical links between WoE and attribution variance and demonstrate the method on LIME and SHAP explanations across varied reference hypotheses.
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.
arXiv:2606. 31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret.
arXiv:2609.24126v1 Announce Type: cross Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...