# How good is the Electricity benchmark for evaluating concept drift adaptation

arXiv: https://arxiv.org/abs/1301.3524 · HTML: https://mlsanity.com/paper/1301.3524/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2013.

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

- [Data-SUITE: Data-centric identification of in-distribution incongruous examples](https://mlsanity.com/paper/2202.08836/) (2022)
- [Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments](https://mlsanity.com/paper/2411.00186/) (2024)
- [Learning from Data Streams: An Overview and Update](https://mlsanity.com/paper/2212.14720/) (2022)
- [A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation](https://mlsanity.com/paper/2411.15616/) (2024)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [An Efficient Model Maintenance Approach for MLOps](https://mlsanity.com/paper/2412.04657/) (2024)
- [The Window Dilemma: Why Concept Drift Detection is Ill-Posed](https://mlsanity.com/paper/2602.06456/) (2026)
- [Sketches for Time-Dependent Machine Learning](https://mlsanity.com/paper/2108.11923/) (2021)

## Abstract (arXiv)

In this correspondence, we will point out a problem with testing adaptive classifiers on autocorrelated data. In such a case random change alarms may boost the accuracy figures. Hence, we cannot be sure if the adaptation is working well.