xplainfi: Feature Importance and Statistical Inference for Machine Learning in R | Lukas Burk et al. | ResearchPod

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

Alex: Welcome to another episode of ResearchPod.

Sam: This episode covers a research paper on xplainfi, an R package for calculating feature importance in machine learning models. The main challenge is measuring which data inputs truly drive predictions when those inputs are linked together in real data.

Alex: So usual ways of checking importance get confused by connected features?

Sam: Yes. Models predict outcomes—like disease risk—using features such as age, blood pressure, or cholesterol. When features correlate and change together, simple shuffling breaks those links and spreads credit misleadingly. xplainfi keeps links intact.

Alex: Like two cholesterol measures that rise together—basic tests might blame both equally, but only one carries the real signal.

Sam: Exactly. It works within mlr3, R's shared tools for models, offering marginal methods like shuffling and conditional ones that respect dependencies. This builds trust in black-box models by pinpointing true drivers.

Alex: Without that, models are risky for real decisions like medicine.

Sam: The paper shows xplainfi matches other tools on tests, adding confidence intervals for certainty. It uses mlr3's learners, tasks, and resampling so users focus on interpretation.

Alex: How does it handle linked features without breaking connections?

Sam: It tests a feature by making small, realistic changes while holding others steady. Picture an orchestra: mute one violin but keep the rest in harmony to hear its true role. Random scrambling ruins the sound. xplainfi's conditional perturbations draw fake values that fit realistically with other features—this is conditional feature importance, or CFI.

Alex: So for cholesterol, it samples new values matching the patient's other stats.

Sam: Yes. Basic permutation feature importance, or PFI, mixes values randomly, creating impossible pairs like high cholesterol with low blood pressure. xplainfi's samplers fix this: one assumes bell-shaped patterns like heights and weights; another maps dependencies with trees for mixed data; a third uses knockoffs that pair fakes for testing.

Alex: It picks the right sampler to avoid shuffle pitfalls.

Sam: It also refits models without one feature—leave-one-covariate-out, or LOCO—to measure performance drops. Leave-one-group-out handles teams. These take more time but give clean measures. Shapley-style methods average a feature's value across combinations, with conditional versions.

Alex: That orchestra analogy shows why harmony matters for diagnosis.

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

Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl

Mar 16, 2026·5:29·arxiv·Original Paper

xplainfi R packageFeature Importance (FI) methodsConditional ImportanceStatistical Inferencemlr3 ecosystem integrationFeature Importance Methods

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5 min

Briefing

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.

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

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

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