A User‐based Visual Analytics Workflow for Exploratory Model Analysis. (10th July 2019)

• Record Type: Journal Article
• Authors: Cashman, Dylan; Humayoun, Shah Rukh; Heimerl, Florian; Park, Kendall; Das, Subhajit; Thompson, John; Saket, Bahador; Mosca, Abigail; Stasko, John; Endert, Alex; Gleicher, Michael; Chang, Remco
• Is Part Of: Computer graphics forum. Volume 38:Number 3(2019)
• Page Start: 185
• Page End: 199
• Publication Date: 2019-07-10
• Subjects: CCS Concepts -- Human‐centered computing → Visual analytics -- Computing methodologies → Model development and analysis -- Mathematics of computing → Exploratory data analysis; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13681
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: 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.