https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
A Unified Approach to Interpreting Model Predictions
Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and discuss it with their co-authors prior to reque
papers.nips.cc
https://christophm.github.io/interpretable-ml-book/
Interpretable Machine Learning
Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.
christophm.github.io
https://shap.readthedocs.io/en/latest/
Welcome to the SHAP documentation — SHAP latest documentation
© Copyright 2018, Scott Lundberg. Revision cd4b3ab2.
shap.readthedocs.io
1. SHAP?
a game theoretic appraoch to explain the output of any machine learning model.
It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations)