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jphall663/awesome-machine-learning-interpretability: A curated list of awesome m ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

jphall663/awesome-machine-learning-interpretability

开源软件地址(OpenSource Url):

https://github.com/jphall663/awesome-machine-learning-interpretability

开源编程语言(OpenSource Language):


开源软件介绍(OpenSource Introduction):

awesome-machine-learning-interpretability Awesome

A curated, but probably biased and incomplete, list of awesome machine learning interpretability resources.

If you want to contribute to this list (and please do!) read over the contribution guidelines, send a pull request, or contact me @jpatrickhall.

An incomplete, imperfect blueprint for a more human-centered, lower-risk machine learning. The resources in this repository can be used to do many of these things today. The resources in this repository should not be considered legal compliance advice. alt-text
Image credit: H2O.ai Machine Learning Interpretability team, https://github.com/h2oai/mli-resources.

Table of Contents

Comprehensive Software Examples and Tutorials

Explainability- or Fairness-Enhancing Software Packages

Browser

Python

R

Machine learning environment management tools

Free Books

Government and Regulatory Documents

Other Interpretability and Fairness Resources and Lists

Review and General Papers

Classes

Interpretable ("Whitebox") or Fair Modeling Packages

C/C++

Python

R

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