Test for non-negligible adverse shifts
This provides a robust metric for model monitoring and data validation, addressing a specific issue in machine learning applications.
The paper tackles the problem of false alarms in statistical tests for dataset shift by proposing a framework called D-SOS to detect adverse shifts based on outlier scores, showing its versatility and practicality on real and simulated data.
Statistical tests for dataset shift are susceptible to false alarms: they are sensitive to minor differences when there is in fact adequate sample coverage and predictive performance. We propose instead a framework to detect adverse dataset shifts based on outlier scores, $\texttt{D-SOS}$ for short. $\texttt{D-SOS}$ holds that the new (test) sample is not substantively worse than the reference (training) sample, and not that the two are equal. The key idea is to reduce observations to outlier scores and compare contamination rates at varying weighted thresholds. Users can define what $\it{worse}$ means in terms of relevant notions of outlyingness, including proxies for predictive performance. Compared to tests of equal distribution, our approach is uniquely tailored to serve as a robust metric for model monitoring and data validation. We show how versatile and practical $\texttt{D-SOS}$ is on a wide range of real and simulated data.