# Suitability of Different Metric Choices for Concept Drift Detection

arXiv: https://arxiv.org/abs/2202.09486 · HTML: https://mlsanity.com/paper/2202.09486/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Understanding Concept Drift](https://mlsanity.com/paper/1704.00362/) (2017)
- [A probability theoretic approach to drifting data in continuous time domains](https://mlsanity.com/paper/1912.01969/) (2019)

## Built on by (most cited first, 8 of 8)

- [Model Based Explanations of Concept Drift](https://mlsanity.com/paper/2303.09331/) (2023)
- [G-Drift MIA: Membership Inference via Gradient-Induced Feature Drift in LLMs](https://mlsanity.com/paper/2604.00419/) (2026)
- [One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments](https://mlsanity.com/paper/2310.15826/) (2023)
- [A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection](https://mlsanity.com/paper/2404.16656/) (2024)
- [Precise Change Point Detection using Spectral Drift Detection](https://mlsanity.com/paper/2205.06507/) (2022)
- [On the Change of Decision Boundaries and Loss in Learning with Concept Drift](https://mlsanity.com/paper/2212.01223/) (2022)
- [A Remark on Concept Drift for Dependent Data](https://mlsanity.com/paper/2312.10212/) (2023)
- [Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation](https://mlsanity.com/paper/2410.04183/) (2024)

## Abstract (arXiv)

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. Many unsupervised approaches for drift detection rely on measuring the discrepancy between the sample distributions of two time windows. This may be done directly, after some preprocessing (feature extraction, embedding into a latent space, etc.), or with respect to inferred features (mean, variance, conditional probabilities etc.). Most drift detection methods can be distinguished in what metric they use, how this metric is estimated, and how the decision threshold is found. In this paper, we analyze structural properties of the drift induced signals in the context of different metrics. We compare different types of estimators and metrics theoretically and empirically and investigate the relevance of the single metric components. In addition, we propose new choices and demonstrate their suitability in several experiments.