Paper Info
Title
A User-based Visual Analytics Workflow for Exploratory Model Analysis.
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.
Year
DOI
Venue
2019
10.1111/cgf.13681
COMPUTER GRAPHICS FORUM
Field
DocType
Volume
Computer science,Visual analytics,Theoretical computer science,Human–computer interaction,Workflow
Journal
38.0
Issue
ISSN
Citations 
3.0
0167-7055
2
PageRank 
References 
Authors
0.35
0
12
Name
Order
Citations
PageRank
Dylan Cashman1103.11
Shah Rukh Humayoun211327.04
Florian Heimerl325215.26
Kendall Park420.35
Subhajit Das5136.22
John Thompson6382.81
Bahador Saket714011.70
Abigail Mosca8122.14
John Stasko95655494.01
Alex Endert1097452.18
Michael Gleicher114378351.49
Remco Chang1298364.96