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Title: GitHub - Sandy1811/interpret: Fit interpretable models. Explain blackbox machine learning.

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Description: Fit interpretable models. Explain blackbox machine learning. - GitHub - Sandy1811/interpret: Fit interpretable models. Explain blackbox machine learning.

Open Graph Description: Fit interpretable models. Explain blackbox machine learning. - GitHub - Sandy1811/interpret: Fit interpretable models. Explain blackbox machine learning.

X Description: Fit interpretable models. Explain blackbox machine learning. - GitHub - Sandy1811/interpret: Fit interpretable models. Explain blackbox machine learning.

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https://patch-diff.githubusercontent.com/Sandy1811/interpret#introducing-the-explainable-boosting-machine-ebm
*https://patch-diff.githubusercontent.com/Sandy1811/interpret#citations
Notebook for reproducing tablehttps://nbviewer.jupyter.org/github/interpretml/interpret/blob/master/benchmarks/EBM%20Classification%20Comparison.ipynb
https://patch-diff.githubusercontent.com/Sandy1811/interpret#supported-techniques
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https://patch-diff.githubusercontent.com/Sandy1811/interpret#acknowledgements
ACKNOWLEDGEMENTS.mdhttps://patch-diff.githubusercontent.com/Sandy1811/interpret/blob/develop/ACKNOWLEDGEMENTS.md
plotlyhttps://github.com/plotly/plotly.py
dashhttps://github.com/plotly/dash
scikit-learnhttps://github.com/scikit-learn/scikit-learn
limehttps://github.com/marcotcr/lime
shaphttps://github.com/slundberg/shap
salibhttps://github.com/SALib/SALib
skope-ruleshttps://github.com/scikit-learn-contrib/skope-rules
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https://patch-diff.githubusercontent.com/Sandy1811/interpret#citations
Paper linkhttps://arxiv.org/pdf/1909.09223.pdf
Paper linkhttps://www.microsoft.com/en-us/research/wp-content/uploads/2017/06/KDD2015FinalDraftIntelligibleModels4HealthCare_igt143e-caruanaA.pdf
Paper linkhttp://www.cs.cornell.edu/~yinlou/papers/lou-kdd13.pdf
Paper linkhttps://www.cs.cornell.edu/~yinlou/papers/lou-kdd12.pdf
Paper linkhttps://arxiv.org/pdf/1810.09092.pdf
Paper linkhttps://arxiv.org/pdf/1710.06169
Paper linkhttps://arxiv.org/pdf/1911.04974.pdf
Paper linkhttp://www-personal.umich.edu/~harmank/Papers/CHI2020_Interpretability.pdf
Paper linkhttps://arxiv.org/pdf/2006.06466.pdf
Paper linkhttps://arxiv.org/pdf/1602.04938.pdf
Paper linkhttp://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
Paper linkhttps://arxiv.org/pdf/1802.03888
Paper linkhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467492/pdf/nihms-1505578.pdf
Paper linkhttps://www.researchgate.net/profile/Will_Usher/publication/312204236_SALib_An_open-source_Python_library_for_Sensitivity_Analysis/links/5ac732d64585151e80a39547/SALib-An-open-source-Python-library-for-Sensitivity-Analysis.pdf?origin=publication_detail
Paper linkhttps://abe.ufl.edu/Faculty/jjones/ABE_5646/2010/Morris.1991%20SA%20paper.pdf
Paper linkhttps://projecteuclid.org/download/pdf_1/euclid.aos/1013203451
Paper linkhttp://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
Linkhttps://plot.ly
Linkhttps://joblib.readthedocs.io/en/latest/
https://patch-diff.githubusercontent.com/Sandy1811/interpret#videos
The Science Behind InterpretML: Explainable Boosting Machinehttps://www.youtube.com/watch?v=MREiHgHgl0k
How to Explain Models with InterpretML Deep Divehttps://www.youtube.com/watch?v=WwBeKMQ0-I8
Black-Box and Glass-Box Explanation in Machine Learninghttps://youtu.be/7uzNKY8pEhQ
Explainable AI explained! By-design interpretable models with Microsofts InterpretMLhttps://www.youtube.com/watch?v=qPn9m30ojfc
https://patch-diff.githubusercontent.com/Sandy1811/interpret#external-links
A gentle introduction to GA2Ms, a white box modelhttps://blog.fiddler.ai/2019/06/a-gentle-introduction-to-ga2ms-a-white-box-model
Model Interpretation with Microsoft’s Interpret MLhttps://medium.com/@sand.mayur/model-interpretation-with-microsofts-interpret-ml-85aa0ad697ae
Explaining Model Pipelines With InterpretMLhttps://medium.com/@mariusvadeika/explaining-model-pipelines-with-interpretml-a9214f75400b
Explain Your Model with Microsoft’s InterpretMLhttps://medium.com/@Dataman.ai/explain-your-model-with-microsofts-interpretml-5daab1d693b4
On Model Explainability: From LIME, SHAP, to Explainable Boostinghttps://everdark.github.io/k9/notebooks/ml/model_explain/model_explain.nb.html
Benchmarking and MLI experiments on the Adult datasethttps://github.com/sayakpaul/Benchmarking-and-MLI-experiments-on-the-Adult-dataset/blob/master/Benchmarking_experiments_on_the_Adult_dataset_and_interpretability.ipynb
Dealing with Imbalanced Data (Mortgage loans defaults)https://mikewlange.github.io/ImbalancedData-/index.html
Kaggle PGA Tour analysis by GAMhttps://www.kaggle.com/juyamagu/pga-tour-analysis-by-gam
https://patch-diff.githubusercontent.com/Sandy1811/interpret#papers-that-use-or-compare-ebms
Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19https://www.medrxiv.org/content/10.1101/2020.06.30.20143651v1.full.pdf
Neural Additive Models: Interpretable Machine Learning with Neural Netshttps://arxiv.org/pdf/2004.13912.pdf
Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Diseasehttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9097874
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionshttps://arxiv.org/pdf/2003.07132v1.pdf
Interpretable Prediction of Goals in Soccerhttp://statsbomb.com/wp-content/uploads/2019/10/decroos-interpretability-statsbomb.pdf
Extending the Tsetlin Machine with Integer-Weighted Clauses for Increased Interpretabilityhttps://arxiv.org/pdf/2005.05131.pdf
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictionhttps://arxiv.org/pdf/2005.04176.pdf
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learninghttps://arxiv.org/pdf/1905.07424.pdf
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