Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
This addresses the challenge of deciding when to retrain models in real-world scenarios where labeled data is unavailable, offering an incremental improvement over existing methods.
The paper tackles the problem of monitoring machine learning models in deployment without labeled data by using non-parametric bootstrapped uncertainty estimates and SHAP values to detect model deterioration and identify its sources, achieving better performance than state-of-the-art methods.
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 deterioration 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 deteriorated despite a shift in the data distribution. To estimate model uncertainty we construct prediction intervals using a novel bootstrap method, which improves upon the work of Kumar & Srivastava (2012). 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 proposed methods, as well as the code used to reproduce our experiments.