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shapper
View on CRAN: Click
here
Download and install shapper package within the R console
Install from CRAN:
install.packages("shapper")
Install from Github:
library("remotes")
install_github("cran/shapper")
Install by package version:
library("remotes")
install_version("shapper", "0.1.3")
Attach the package and use:
library("shapper")
Maintained by
Szymon Maksymiuk
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-02
Latest Update: 2020-08-28
Description:
Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
How to cite:
Szymon Maksymiuk (2019). shapper: Wrapper of Python Library 'shap'. R package version 0.1.3, https://cran.r-project.org/web/packages/shapper
Previous versions and publish date:
Other packages that cited shapper R package
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Other R packages that shapper depends,
imports, suggests or enhances
Functions, R codes and Examples using
the shapper R package
Some associated functions: individual_variable_effect . install_shap . plot.individual_variable_effect . print.individual_variable_effect . theme_drwhy .
Some associated R codes: individual_variable_effect.R . install_shap.R . onLoad.R . plot_individual_variable_effect.R . print_individual_variable_effect.R . theme_drwhy.R . Full shapper package functions and examples
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