LIME-Based Explanations with Interpretable Inputs Based on Ceteris Paribus Profiles

Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) ) in which interpretable inputs are created based on local rather than global behaviour of each original feature.


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0.3.12 by Mateusz Staniak, a year ago

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Authors: Mateusz Staniak [aut, cre] , Przemyslaw Biecek [aut] , Krystian Igras [ctb] , Alicja Gosiewska [ctb]

Documentation:   PDF Manual  

GPL license

Imports glmnet, ggplot2, partykit, ingredients

Suggests covr, knitr, rmarkdown, randomForest, DALEX, testthat

Suggested by DALEXtra.

See at CRAN