awesome-interpretable-machine-learning

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Model Interpreter Guide

An exhaustive collection of resources and techniques facilitating model interpretability in machine learning.

stars: 909
forks: 139
watching: 52
awesome list: 1
entries: 111

What's in the list

111 links in 23 sections, with live GitHub stats.activeno commit in 2y

Links

https://awesome.re][https://awesome.re/badge.svg]]
Awesome Interpretable Machine Learning [[
https://dx.doi.org/10.1214/07-AOAS148
https://dx.doi.org/10.1145/2594473.2594475
http://www.kdd.org/exploration_files/V15-01-01-Freitas.pdf
https://arxiv.org/pdf/1511.01644
https://dx.doi.org/10.1214/15-AOAS848
https://arxiv.org/pdf/1711.04574
https://arxiv.org/pdf/1912.04695
https://github.com/12wang3/mllp
Code:
22over 2 years

Extremely randomized trees / (2006) Extremely randomized trees by Pierre Geurts, Damien Ernst, Louis Wehenkel

https://dx.doi.org/10.1007/s10994-006-6226-1

Random ferns / (2015) rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning by Miron B. Kursa

Links

R code

https://cran.r-project.org/web/packages/randomForestExplainer/index.html
CRAN
https://github.com/MI2DataLab/randomForestExplainer
Code:
230over 2 years

Links

https://github.com/ehrlinger/ggRandomForests/raw/master/vignettes/randomForestSRC-Survival.pdf
Paper (vignette)
146almost 2 years

R code

https://cran.r-project.org/web/packages/ggRandomForests/index.html
CRAN
https://github.com/ehrlinger/ggRandomForests
Code:
146almost 2 years

Links

Debate, Interpretability is necessary in machine learning

https://www.youtube.com/watch?v=2hW05ZfsUUo

2018 (contains links to and )

https://sites.google.com/view/whi2018
https://arxiv.org/html/1807.01308
Proceedings

2017 (contains links to and )

2016 (contains links to )

https://sites.google.com/site/2016whi/
https://arxiv.org/html/1607.02531
Proceedings or [[

2019 (links below may get prefixed by 2019 later on)

2018

2017

https://www.fatml.org/schedule/2017

2016

2015

https://www.fatml.org/schedule/2015

2014

https://www.fatml.org/schedule/2014

2019 (links below may get prefixed by 2019 later on)

http://www.aies-conference.com/accepted-papers/

2018

http://www.aies-conference.com/2018/accepted-papers/
http://www.aies-conference.com/2018/accepted-student-papers/
** Software Software related to papers is mentioned along with each publication. Here only standalone software is included

Links

https://cran.r-project.org/web/packages/DALEX/DALEX.pdf
CRAN
https://github.com/pbiecek/DALEX
Code:
1.4K2 years
https://github.com/TeamHG-Memex/eli5
Code:
2.8Kover 4 years
https://eli5.readthedocs.io/en/latest/
https://cran.r-project.org/web/packages/forestmodel/index.html
CRAN
https://github.com/NikNakk/forestmodel
Code:
42almost 3 years
https://cran.r-project.org/web/packages/fscaret/
CRAN
https://cran.r-project.org/web/packages/fscaret/vignettes/fscaret.pdf
Tutorial:
https://cran.r-project.org/web/packages/iml/
CRAN
https://github.com/christophM/iml
Code:
494almost 2 years
http://joss.theoj.org/papers/10.21105/joss.00786
Publication:
https://github.com/microsoft/interpret
Code:
6.3Kalmost 2 years
https://github.com/thomasp85/lime
4864 years
https://github.com/aerdem4/lofo-importance
Code:
821over 2 years
https://github.com/tensorflow/lucid
Code:
4.7Kover 3 years
https://cran.r-project.org/web/packages/praznik/index.html
CRAN
https://notabug.org/mbq/praznik
Code:
https://github.com/DistrictDataLabs/yellowbrick
Code:
4.3K2 years
http://www.scikit-yb.org/en/latest/

list of resources by Patrick Hall

https://github.com/jphall663/awesome-machine-learning-interpretability
3.7Kalmost 2 years

XAI resources by Przemysław Biecek

https://github.com/pbiecek/xai_resources
819over 4 years

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