Keywords: dataset shift, model drift, robust statistics, goodness-of-fit test, out-of-distribution detection
TL;DR: robust statistical test to detect adverse shifts based on outlier scores
Abstract: 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 shifts based on outlier scores, D-SOS for short. 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 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 D-SOS is on a wide range of real and simulated data.
Supplementary Material: zip