# A User-based Visual Analytics Workflow for Exploratory Model Analysis

arXiv: https://arxiv.org/abs/1809.10782 · HTML: https://mlsanity.com/paper/1809.10782/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 12 of 12)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks](https://mlsanity.com/paper/2502.10526/) (2025)
- [Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles](https://mlsanity.com/paper/2208.09665/) (2022)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models](https://mlsanity.com/paper/2101.11970/) (2021)
- [ATWL: A Formal Language for Representing, Comparing, and Reusing Visual Analytics Workflows](https://mlsanity.com/paper/2605.25489/) (2026)
- [Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation](https://mlsanity.com/paper/2104.07377/) (2021)
- [Circles: Inter-Model Comparison of Multi-Classification Problems with High Number of Classes](https://mlsanity.com/paper/2309.05672/) (2023)
- [Who Leads? Comparing Human-Centric and Model-Centric Strategies for Defining ML Target Variables](https://mlsanity.com/paper/2510.25974/) (2025)
- [SmartIterator: Visual Analytics Workflows for Supervising Unsupervised Data Grouping](https://mlsanity.com/paper/2605.28219/) (2026)

## Abstract (arXiv)

Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.