lime: Local Interpretable Model-Agnostic Explanations

When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <doi:10.48550/arXiv.1602.04938>.

Version: 0.5.3
Imports: glmnet, stats, ggplot2, tools, stringi, Matrix, Rcpp, assertthat, methods, grDevices, gower
LinkingTo: Rcpp, RcppEigen
Suggests: xgboost, testthat, mlr, h2o, text2vec, MASS, covr, knitr, rmarkdown, sessioninfo, magick, keras, htmlwidgets, shiny, shinythemes, ranger
Published: 2022-08-19
DOI: 10.32614/CRAN.package.lime
Author: Emil Hvitfeldt ORCID iD [aut, cre], Thomas Lin Pedersen ORCID iD [aut], Michaël Benesty [aut]
Maintainer: Emil Hvitfeldt <emilhhvitfeldt at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
In views: MachineLearning
CRAN checks: lime results


Reference manual: lime.pdf
Vignettes: Understanding lime


Package source: lime_0.5.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lime_0.5.3.tgz, r-oldrel (arm64): lime_0.5.3.tgz, r-release (x86_64): lime_0.5.3.tgz, r-oldrel (x86_64): lime_0.5.3.tgz
Old sources: lime archive

Reverse dependencies:

Reverse imports: grafzahl
Reverse suggests: DALEXtra, innsight


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