# Diagnosing Concept Drift with Visual Analytics

arXiv: https://arxiv.org/abs/2007.14372 · HTML: https://mlsanity.com/paper/2007.14372/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Diagnosing Ensemble Few-Shot Classifiers](https://mlsanity.com/paper/2206.04372/) (2022)
- [A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision](https://mlsanity.com/paper/2308.05168/) (2023)
- [One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments](https://mlsanity.com/paper/2310.15826/) (2023)
- [DeepLens: Interactive Out-of-distribution Data Detection in NLP Models](https://mlsanity.com/paper/2303.01577/) (2023)
- [Contrastive Identification of Covariate Shift in Image Data](https://mlsanity.com/paper/2108.08000/) (2021)
- [VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations](https://mlsanity.com/paper/2104.02797/) (2021)
- [Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles](https://mlsanity.com/paper/2208.09665/) (2022)
- [ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis](https://mlsanity.com/paper/2303.09657/) (2023)
- [DenDrift: A Drift-Aware Algorithm for Host Profiling](https://mlsanity.com/paper/2110.01221/) (2021)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)
- [d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement](https://mlsanity.com/paper/2507.11960/) (2025)
- [WidgetVA: A Widget-Centric Framework and Benchmark for Agentic Visual Analytics](https://mlsanity.com/paper/2609.24094/) (2026)

## Abstract (arXiv)

Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccurate. While a variety of automated methods have been developed to identify when concept drift occurs, there is limited support for analysts who need to understand and correct their models when drift is detected. In this paper, we present a visual analytics method, DriftVis, to support model builders and analysts in the identification and correction of concept drift in streaming data. DriftVis combines a distribution-based drift detection method with a streaming scatterplot to support the analysis of drift caused by the distribution changes of data streams and to explore the impact of these changes on the model's accuracy. A quantitative experiment and two case studies on weather prediction and text classification have been conducted to demonstrate our proposed tool and illustrate how visual analytics can be used to support the detection, examination, and correction of concept drift.