# Data+Shift: Supporting visual investigation of data distribution shifts by data scientists

arXiv: https://arxiv.org/abs/2204.14025 · HTML: https://mlsanity.com/paper/2204.14025/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 29 Apr 2022.

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

- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data](https://mlsanity.com/paper/2007.15272/) (2020)
- [Automatic Model Monitoring for Data Streams](https://mlsanity.com/paper/1908.04240/) (2019)
- [SAX Navigator: Time Series Exploration through Hierarchical Clustering](https://mlsanity.com/paper/1908.05505/) (2019)
- [Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs](https://mlsanity.com/paper/2009.03385/) (2020)

## Built on by (most cited first, 3 of 3)

- [DeepLens: Interactive Out-of-distribution Data Detection in NLP Models](https://mlsanity.com/paper/2303.01577/) (2023)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [Lightweight Automated Feature Monitoring for Data Streams](https://mlsanity.com/paper/2207.08640/) (2022)

## Abstract (arXiv)

Machine learning on data streams is increasingly more present in multiple domains. However, there is often data distribution shift that can lead machine learning models to make incorrect decisions. While there are automatic methods to detect when drift is happening, human analysis, often by data scientists, is essential to diagnose the causes of the problem and adjust the system. We propose Data+Shift, a visual analytics tool to support data scientists in the task of investigating the underlying factors of shift in data features in the context of fraud detection. Design requirements were derived from interviews with data scientists. Data+Shift is integrated with JupyterLab and can be used alongside other data science tools. We validated our approach with a think-aloud experiment where a data scientist used the tool for a fraud detection use case.