midr: Learning from Black-Box Models by Maximum Interpretation
Decomposition
The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) <doi:10.48550/arXiv.2506.08338>.
| Version: |
0.5.2 |
| Imports: |
graphics, grDevices, Rcpp, RcppEigen, rlang, stats, utils |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
datasets, ggplot2, khroma, knitr, RColorBrewer, rmarkdown, scales, shapviz, testthat, viridisLite |
| Published: |
2025-09-07 |
| DOI: |
10.32614/CRAN.package.midr |
| Author: |
Ryoichi Asashiba [aut, cre],
Hirokazu Iwasawa [aut],
Reiji Kozuma [ctb] |
| Maintainer: |
Ryoichi Asashiba <ryoichi.asashiba at gmail.com> |
| BugReports: |
https://github.com/ryo-asashi/midr/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/ryo-asashi/midr,
https://ryo-asashi.github.io/midr/ |
| NeedsCompilation: |
yes |
| Citation: |
midr citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
midr results |
Documentation:
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