The paper introduces PRIME, a new variable‑importance measure that addresses two limitations of the existing proportional marginal effects (PME) method: it is applicable to stochastic models and reduces computational cost. PRIME achieves this by employing a model‑free variable‑selection technique to isolate active variables before computing importance. Experiments on synthetic test functions and real datasets demonstrate that PRIME accurately and reliably quantifies variable importance from noisy input‑output data.
arXiv:2610.35740v1 Announce Type: cross Abstract: Computing local-level variable importance (VI) is critical for understanding machine learning models. Local permutation-based VI metrics offer a simp...
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...
Computing local-level variable importance (VI) is critical for understanding machine learning models. Local permutation-based VI metrics offer a simple yet powerful approach, measuring how much a mode...
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:2512. 13892v3 Announce Type: replace-cross Abstract: Reliable estimation of feature contributions in machine learning models is essential for transparency, algorithmic fairness, and regulatory compliance.
The paper introduces MAS (Model‑Agnostic Selection with agent‑X importance), a new method for controlling the false discovery rate (FDR) in variable selection that does not rely on specific model assumptions. MAS uses sample splitting, a novel agent‑X importance measure, a resplit‑per‑covariate strategy, and data aggregation to achieve asymptotic FDR control while improving computational efficiency and power. The authors provide theoretical guarantees and demonstrate competitive performance through extensive simulations and real‑data analyses.
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: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...
The paper introduces MCIR-M, a global feature‑importance method that accounts for feature dependence by conditioning each feature on its strongly dependent neighbours and computing a normalized Mutual Correlation Impact Ratio. MCIR-M yields a population score between 0 and 1, reaching zero when a feature is exactly conditionally redundant, and can be estimated with only a fraction of the data. Experiments on synthetic redundancy setups and the UCI HAR benchmark show that MCIR-M produces more stable rankings under near‑duplicate predictors compared to existing methods such as SHAP, SAGE, HSIC, and MI‑based scores.
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...
arXiv:2606. 10770v1 Announce Type: cross Abstract: Variable importance produced by Random Forests (RF) is used widely in statistical data analysis, and has played an important role in a variety of tasks such as assisting model interpretation, model selection and diagnosis, and cost-bounded learning etc.
arXiv:2411. 08821v4 Announce Type: replace-cross Abstract: Global variable importance measures are commonly used to interpret the results of machine learning models.