# Understanding Concept Drift

arXiv: https://arxiv.org/abs/1704.00362 · HTML: https://mlsanity.com/paper/1704.00362/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2017.

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

- [Model Based Explanations of Concept Drift](https://mlsanity.com/paper/2303.09331/) (2023)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [Suitability of Different Metric Choices for Concept Drift Detection](https://mlsanity.com/paper/2202.09486/) (2022)
- [One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments](https://mlsanity.com/paper/2310.15826/) (2023)
- [Adversarial Attacks for Drift Detection](https://mlsanity.com/paper/2411.16591/) (2024)
- [Learner-based Concept Drift Detection: Analysis and Evaluation](https://mlsanity.com/paper/2606.20216/) (2026)
- [Precise Change Point Detection using Spectral Drift Detection](https://mlsanity.com/paper/2205.06507/) (2022)
- [Causal Explanation of Concept Drift -- A Truly Actionable Approach](https://mlsanity.com/paper/2507.23389/) (2025)

## Abstract (arXiv)

Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is important to develop the capacity to describe and analyze it. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift in marginal distributions. We present quantitative drift analysis techniques along with methods for communicating their results. We demonstrate their effectiveness by application to three real-world learning tasks.