midr: Learning from Black-Box Models by Maximum InterpretationDecomposition
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 + fileLICENSE |
| 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 |
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