# Detecting model drift using polynomial relations

arXiv: https://arxiv.org/abs/2110.12506 · HTML: https://mlsanity.com/paper/2110.12506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Detection of data drift and outliers affecting machine learning model performance over time](https://mlsanity.com/paper/2012.09258/) (2020)
- [FreaAI: Automated extraction of data slices to test machine learning models](https://mlsanity.com/paper/2108.05620/) (2021)
- [Machine Learning Model Drift Detection Via Weak Data Slices](https://mlsanity.com/paper/2108.05319/) (2021)

## Built on by (most cited first, 1 of 1)

- [Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach](https://mlsanity.com/paper/2201.00355/) (2022)

## Abstract (arXiv)

Machine learning models serve critical functions, such as classifying loan applicants as good or bad risks. Each model is trained under the assumption that the data used in training and in the field come from the same underlying unknown distribution. Often, this assumption is broken in practice. It is desirable to identify when this occurs, to minimize the impact on model performance. We suggest a new approach to detecting change in the data distribution by identifying polynomial relations between the data features. We measure the strength of each identified relation using its R-square value. A strong polynomial relation captures a significant trait of the data which should remain stable if the data distribution does not change. We thus use a set of learned strong polynomial relations to identify drift. For a set of polynomial relations that are stronger than a given threshold, we calculate the amount of drift observed for that relation. The amount of drift is measured by calculating the Bayes Factor for the polynomial relation likelihood of the baseline data versus field data. We empirically validate the approach by simulating a range of changes, and identify drift using the Bayes Factor of the polynomial relation likelihood change.