xplainfi: Feature Importance and Statistical Inference for Machine Learning in R
This provides researchers and practitioners in R with a modular toolkit for feature importance analysis, though it is incremental as it builds on existing methods and ecosystems.
The authors tackled the lack of comprehensive feature importance and statistical inference tools in R by introducing xplainfi, an R package that implements various global, loss-based methods and demonstrates consistency with existing implementations while offering competitive runtime performance.
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.