There are 9 repositories under machine-learning-interpretability topic.
A curated list of awesome responsible machine learning resources.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
H2O.ai Machine Learning Interpretability Resources
Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
An interpretable machine learning pipeline over knowledge graphs
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Article for Special Edition of Information: Machine Learning with Python
Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539
Overview of machine learning interpretation techniques and their implementations
INVASE: Instance-wise Variable Selection . For more details, read the paper "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019.
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
Demonstration of InterpretME, an interpretable machine learning pipeline
Default Risk Prediction from bank dataset with Interpretable Machine Learning