xplainfi: Feature Importance and Statistical Inference for Machine Learning in R

2026-03-16

AI Summary

This work addresses the lack of a unified R framework for computing conditional feature importance and conducting associated statistical inference, which hinders reliable interpretation of machine learning models. To bridge this gap, the authors introduce xplainfi, an R package built on the mlr3 ecosystem that features a novel modular conditional sampling architecture. It integrates diverse samplers—including Gaussian approximations, adversarial random forests, conditional inference trees, and knockoffs—making it suitable for both continuous and mixed-type data. The package supports multiple global importance measures such as permutation importance, conditional and marginal Shapley values, and leave-one-covariate-out methods. Rigorous statistical inference is enabled through variance-corrected confidence intervals and a conditional predictive impact framework. Empirical evaluations demonstrate that xplainfi yields importance scores consistent with existing approaches while maintaining competitive computational efficiency. The package is publicly available on CRAN.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We introduce xplainfi, an R package built on top of the mlr3 ecosystem for global, loss-based feature importance methods for machine learning models. Various feature importance methods exist in R, but significant gaps remain, particularly regarding conditional importance methods and associated statistical inference procedures. The package implements permutation feature importance, conditional feature importance, relative feature importance, leave-one-covariate-out, and generalizations thereof, and both marginal and conditional Shapley additive global importance methods. It provides a modular conditional sampling architecture based on Gaussian distributions, adversarial random forests, conditional inference trees, and knockoff-based samplers, which enable conditional importance analysis for continuous and mixed data. Statistical inference is available through multiple approaches, including variance-corrected confidence intervals and the conditional predictive impact framework. We demonstrate that xplainfi produces importance scores consistent with existing implementations across multiple simulation settings and learner types, while offering competitive runtime performance. The package is available on CRAN and provides researchers and practitioners with a comprehensive toolkit for feature importance analysis and model interpretation in R.

Problem

• feature importance
• conditional importance
• statistical inference
• model interpretation
• machine learning

Innovation

• conditional feature importance
• statistical inference
• Shapley additive importance
• modular conditional sampling
• model interpretability