Machine learning models often mispredict, and it is hard to tell when and why. We developed a technique, MMD, that systematically discovers rules that characterize a subset of the input space of a machine learning model where the model is more likely to mispredict.
Our work has been published at the International Conference on Foundations in Software Engineering (FSE'21): J. Cito, I. Dillig, S. Kim, V. Murali, S. Chandra, Explaining Mispredictions of Machine Learning Models using Rule Induction.
@inproceedings{explaining_mispredictions:21,
title={Explaining mispredictions of machine learning models using rule induction},
author={Cito, J{\"u}rgen and Dillig, Isil and Kim, Seohyun and Murali, Vijayaraghavan and Chandra, Satish},
booktitle={Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
pages={716--727},
year={2021}
}- Python 3.8
- Pandas
MMD is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) (e.g., FAIR) licensed, as found in the LICENSE file.