Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
Carlos Mougan, Dan Saattrup Nielsen
Abstract
Monitoring machine learning models once they are deployed is challenging. It is even more challenging to decide when to retrain models in real-case scenarios when labeled data is beyond reach, and monitoring performance metrics becomes unfeasible. In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deteri- oration when target labels are not available. Classical methods are purely aimed at detecting distribution shift, which can lead to false positives in the sense that the model has not deterio- rated despite a shift in the data distribution. To estimate model uncertainty we construct prediction intervals using a novel bootstrap method, which improves previous state-of-the-art work. We show that both our model deterioration detection system as well as our uncertainty estimation method achieve better performance than the current state-of-the-art. Finally, we use explainable AI techniques to gain an understanding of the drivers of model deterioration. We release an open source Python package, doubt, which implements our pro- posed methods, as well as the code used to reproduce our experiments.
BibTeX
@article{Mougan_Nielsen_2023, title={Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26755}, DOI={10.1609/aaai.v37i12.26755}, abstractNote={Monitoring machine learning models once they are deployed
is challenging. It is even more challenging to decide when
to retrain models in real-case scenarios when labeled data is
beyond reach, and monitoring performance metrics becomes
unfeasible. In this work, we use non-parametric bootstrapped
uncertainty estimates and SHAP values to provide explainable
uncertainty estimation as a technique that aims to monitor
the deterioration of machine learning models in deployment
environments, as well as determine the source of model deteri-
oration when target labels are not available. Classical methods
are purely aimed at detecting distribution shift, which can lead
to false positives in the sense that the model has not deterio-
rated despite a shift in the data distribution. To estimate model
uncertainty we construct prediction intervals using a novel
bootstrap method, which improves previous state-of-the-art
work. We show that both our model deterioration detection
system as well as our uncertainty estimation method achieve
better performance than the current state-of-the-art. Finally,
we use explainable AI techniques to gain an understanding
of the drivers of model deterioration. We release an open
source Python package, doubt, which implements our pro-
posed methods, as well as the code used to reproduce our
experiments.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mougan, Carlos and Nielsen, Dan Saattrup}, year={2023}, month={Jun.}, pages={15037-15045} }