Local Interpretable (Model-Agnostic) Visual Explanations

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) . More details can be found in Staniak, Biecek (2018) .


live: Local Interpretable (Model-agnostic) Visual Explanations

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Installation

To get started, install stable CRAN version:

install.packages("live")

or the development version:

devtools::install_github("ModelOriented/live")

See the latest changes.

Features coming up next:

  • better support for comparing explanations for different models / different instances,

  • improved Shiny application (see live_shiny function in development version).

If you have any bug reports, feature requests or ideas to improve the methodology, feel free to leave an issue.

Materials

Find the paper about live and breakDown in R Journal.

Website: https://mi2datalab.github.io/live/

Conference talks on live: Wrocław 2018, Berlin 2017.

Python implementation of LIME and info about the method: https://github.com/marcotcr/lime

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("live")

1.5.13 by Mateusz Staniak, 7 years ago


https://github.com/ModelOriented/live


Report a bug at https://github.com/ModelOriented/live/issues


Browse source code at https://github.com/cran/live


Authors: Mateusz Staniak [cre, aut] , Przemysław Biecek [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports mlr, dplyr, breakDown, data.table, forestmodel, shiny, MASS, ggplot2, gower, e1071

Suggests knitr, rmarkdown, testthat, glmnet, covr, DALEX, RWeka, mda, modeltools


See at CRAN