awesome-interpretable-machine-learning

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Language: Python
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data-scienceexplainable-aiinterpretable-aiinterpretable-machine-learninginterpretable-mlmachine-learningxai

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 21 7 months ago Code:

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
https://dx.doi.org/10.18637/jss.v061.i10
https://cran.r-project.org/web/packages/rFerns
https://notabug.org/mbq/rFerns
https://dx.doi.org/10.1186/1471-2105-8-25
https://dx.doi.org/10.1186/1471-2105-9-307
https://arxiv.org/pdf/1801.01489
https://github.com/aaronjfisher/mcr 8 almost 5 years ago
https://arxiv.org/pdf/1905.03151
https://arxiv.org/pdf/1804.06620
https://github.com/giuseppec/featureImportance 33 over 3 years ago
http://explained.ai/rf-importance/index.html
https://eli5.readthedocs.io/en/latest/blackbox/permutation_importance.html
http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
https://dx.doi.org/10.1007/11732242_9
https://pdfs.semanticscholar.org/d72f/f5063520ce4542d6d9b9e6a4f12aafab6091.pdf
http://www.jmlr.org/papers/volume13/brown12a/brown12a.pdf
https://github.com/Craigacp/FEAST 69 almost 2 years ago Code:
http://www.cs.man.ac.uk/~gbrown/publications/pocockPhDthesis.pdf
https://arxiv.org/pdf/1711.08421
https://arxiv.org/pdf/1711.08477
https://dx.doi.org/10.18637/jss.v036.i11
https://cran.r-project.org/web/packages/Boruta/
https://notabug.org/mbq/Boruta/ Code (official, R):
https://github.com/scikit-learn-contrib/boruta_py 1,495 about 2 months ago Code (Python):
https://cran.r-project.org/web/packages/Boruta/vignettes/inahurry.pdf
https://pdfs.semanticscholar.org/a83b/ddb34618cc68f1014ca12eef7f537825d104.pdf
http://www.jmlr.org/papers/special/feature03.html
https://papers.nips.cc/paper/2728-result-analysis-of-the-nips-2003-feature-selection-challenge.pdf Paper:
http://clopinet.com/isabelle/Projects/NIPS2003/ Website
http://www.jmlr.org/papers/volume8/nilsson07a/nilsson07a.pdf
http://www.feat.engineering/index.html
https://bookdown.org/max/FES/
https://github.com/topepo/FES 716 11 months ago
https://www.slideshare.net/HJvanVeen/feature-engineering-72376750 Slides:
https://arxiv.org/pdf/1711.09576
https://arxiv.org/pdf/1711.09784
http://www.aies-conference.com/2018/contents/papers/main/AIES_2018_paper_96.pdf
http://had.co.nz/stat645/model-vis.pdf
http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html
https://journal.r-project.org/archive/2017/RJ-2017-016/RJ-2017-016.pdf pdp: An R Package for Constructing Partial Dependence Plots
https://journal.r-project.org/archive/2016-2/tang-horikoshi-li.pdf
https://cran.r-project.org/web/packages/ggfortify/index.html CRAN
https://rawgit.com/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer_Master_thesis.pdf Master thesis

R code
https://cran.r-project.org/web/packages/randomForestExplainer/index.html CRAN
https://github.com/MI2DataLab/randomForestExplainer 230 6 months ago Code:
https://github.com/ehrlinger/ggRandomForests/raw/master/vignettes/randomForestSRC-Survival.pdf 146 almost 2 years ago Paper (vignette)

R code
https://cran.r-project.org/web/packages/ggRandomForests/index.html CRAN
https://github.com/ehrlinger/ggRandomForests 146 almost 2 years ago Code:
http://people.csail.mit.edu/beenkim/papers/BeenK_FinaleDV_ICML2017_tutorial.pdf Slides:
https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/Show-Me-Your-Model-tools-for-visualisation-of-statistical-models Video:
https://www.youtube.com/watch?v=DiWkKqZChF0 Video:
https://speakerdeck.com/sritchie/just-so-stories-for-ai-explaining-black-box-predictions Slides:
https://www.youtube.com/watch?v=B3PtcF-6Dtc Video:
https://docs.google.com/presentation/d/e/2PACX-1vR05kpagAbL5qo1QThxwu44TI5SQAws_UFVg3nUAmKp39uNG0xdBjcMA-VyEeqZRGGQtt0CS5h2DMTS/embed?start=false&loop=false&delayms=3000 Slides:
https://www.youtube.com/watch?v=nDF7_8FOhpI Video:
https://github.com/ianozsvald/data_science_delivered/blob/master/ml_explain_regression_prediction.ipynb 543 over 3 years ago Associated notebook on explaining regression predictions:
https://www.youtube.com/watch?v=kbj3llSbaVA Video:
http://gael-varoquaux.info/interpreting_ml_tuto/ Slides:
http://interpretable.ml/

Debate, Interpretability is necessary in machine learning
https://www.youtube.com/watch?v=2hW05ZfsUUo
https://sites.google.com/view/whi2018
https://arxiv.org/html/1807.01308 Proceedings
https://sites.google.com/view/whi2017/home
https://arxiv.org/html/1708.02666 Proceedings
https://sites.google.com/site/2016whi/
https://arxiv.org/html/1607.02531 Proceedings or [[
https://blackboxnlp.github.io/
https://blackboxnlp.github.io/program.html

2018
https://blackboxnlp.github.io/2018
https://blackboxnlp.github.io/program.html
https://arxiv.org/search/advanced?advanced=&terms-0-operator=AND&terms-0-term=BlackboxNLP&terms-0-field=comments&terms-1-operator=OR&terms-1-term=Analyzing+interpreting+neural+networks+NLP&terms-1-field=comments&classification-physics_archives=all&date-filter_by=all_dates&date-year=&date-from_date=&date-to_date=&date-date_type=submitted_date&abstracts=show&size=200&order=-announced_date_first][List [[ of papers]]
https://www.fatml.org/schedule/2018

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

2016
https://www.fatml.org/schedule/2016
https://www.fatml.org/schedule/2016

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

2014
https://www.fatml.org/schedule/2014
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
https://cran.r-project.org/web/packages/DALEX/DALEX.pdf CRAN
https://github.com/pbiecek/DALEX 1,365 about 1 month ago Code:
https://github.com/TeamHG-Memex/eli5 2,753 over 2 years ago Code:
https://eli5.readthedocs.io/en/latest/
https://cran.r-project.org/web/packages/forestmodel/index.html CRAN
https://github.com/NikNakk/forestmodel 40 10 months ago Code:
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 491 3 days ago Code:
http://joss.theoj.org/papers/10.21105/joss.00786 Publication:
https://github.com/microsoft/interpret 6,237 5 days ago Code:
https://github.com/thomasp85/lime 481 about 2 years ago
https://github.com/aerdem4/lofo-importance 816 9 months ago Code:
https://github.com/tensorflow/lucid 4,653 over 1 year ago Code:
https://cran.r-project.org/web/packages/praznik/index.html CRAN
https://notabug.org/mbq/praznik Code:
https://github.com/DistrictDataLabs/yellowbrick 4,270 about 1 year ago Code:
http://www.scikit-yb.org/en/latest/

list of resources by Patrick Hall
https://github.com/jphall663/awesome-machine-learning-interpretability 3,619 2 days ago

XAI resources by Przemysław Biecek
https://github.com/pbiecek/xai_resources 816 over 2 years ago

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