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 
[aut, cre],
Thomas Lin Pedersen
[aut],
Michaël Benesty [aut] |
| Maintainer: |
Emil Hvitfeldt <emilhhvitfeldt at gmail.com> |
| BugReports: |
https://github.com/thomasp85/lime/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://lime.data-imaginist.com, https://github.com/thomasp85/lime |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| In views: |
MachineLearning |
| CRAN checks: |
lime results |