agosiewska/shapper: Wrapper of Python Library 'shap'

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) <arXiv:1705.07874> 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'.

Getting started

Package details

Maintainer
LicenseGPL
Version0.1.2
URL https://github.com/ModelOriented/shapper
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("agosiewska/shapper")
agosiewska/shapper documentation built on Oct. 5, 2019, 1:26 a.m.