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Title: GitHub - afairless/shap_tutorial: How to use SHAP to interpret machine learning models

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Description: How to use SHAP to interpret machine learning models - afairless/shap_tutorial

Open Graph Description: How to use SHAP to interpret machine learning models - afairless/shap_tutorial

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A Unified Approach to Interpreting Model Predictionshttps://arxiv.org/abs/1705.07874
Consistent feature attribution for tree ensembleshttps://arxiv.org/abs/1706.06060
Consistent Individualized Feature Attribution for Tree Ensembleshttps://arxiv.org/abs/1802.03888
A game theoretic approach to explain the output of any machine learning model.https://github.com/slundberg/shap
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 5.9 Shapley Valueshttps://christophm.github.io/interpretable-ml-book/shapley.html
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 5.10 SHAP (SHapley Additive exPlanationshttps://christophm.github.io/interpretable-ml-book/shap.html
Interpretable Machine Learning with XGBoosthttps://towardsdatascience.com/interpretable-machine-learning-with-xgboost-9ec80d148d27?gi=187ef710fdda
Explain Your Model with the SHAP Valueshttps://towardsdatascience.com/explain-your-model-with-the-shap-values-bc36aac4de3d
Interpretable Machine Learning: A Guide for Making Black Box Models Explainable.https://christophm.github.io/interpretable-ml-book/
Explanation in Artificial Intelligence: Insights from the Social Scienceshttps://arxiv.org/abs/1706.07269
Interpretability and the promise of healthcare AIhttps://www.geneia.com/blog/2020/january/interpretability-and-the-promise-of-healthcare-ai
Interpretability and the promise of healthcare AIhttps://web.archive.org/web/20210418122745/https://www.geneia.com/blog/2020/january/interpretability-and-the-promise-of-healthcare-ai
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