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) <doi:10.1145/2939672.2939778>) in which interpretable
inputs are created based on local rather than global behaviour of each original feature.
localModel: Local Explanations of Machine Learning Models for Tabular Data.
localModel is a successor to the
live package. It implements a variant of LIME method for explaining single predictions of black box machine learning models for tabular data.
Interpretable features are created based on Ceteris Paribus plots.
Details of the methodology are described in the vignette.
To get started, install the newest version from GitHub by using the code below.
Please do not use the
devtools package, since it is affected a bug which makes
localModel installation impossible.
This issue was solved in the Github version of
To get help, see examples and details of the methodology, please refer to package website and vignettes.
Work on this package is financially supported by the NCN Opus grant 2017/27/B/ST6/01307.
Functions in localModel
|identity_kernel||LIME kernel that treats all observations as equally similar to the observation of interest.|
|gaussian_kernel||LIME kernel from the original article with sigma = 1.|
|print.local_surrogate_explainer||Generic print function for local surrogate explainers|
|individual_surrogate_model||LIME-like explanations based on Ceteris Paribus curves|
|plot.local_surrogate_explainer||Generic plot function for local surrogate explainers|
|localModel||localModel: LIME-like explanations with interpretable features based on Ceteris Paribus profiles|
Vignettes of localModel
Last month downloads
|Packaged||2019-04-13 09:45:17 UTC; mstaniak|
|Date/Publication||2019-04-14 11:02:43 UTC|
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