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Local Interpretable (Model-Agnostic) Visual Explanations

v1.5.13 · Jan 14, 2020 · MIT + file LICENSE

Description

Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>. More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespace in Imports field not imported from: ‘e1071’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespace in Imports field not imported from: ‘e1071’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies mlr dplyr breakDown data.table forestmodel shiny MASS ggplot2 gower e1071 live

Version History

new 1.5.13 Mar 9, 2026