A User-based Visual Analytics Workflow for Exploratory Model Analysis

TL;DR

This paper introduces a visual analytics workflow for Exploratory Model Analysis (EMA), enabling users to generate, evaluate, and select robust predictive models tailored for deployment, distinct from traditional data insight exploration.

Contribution

The work presents a novel visual analytics system workflow specifically designed for EMA, along with a user study and use cases demonstrating its effectiveness.

Findings

Users can generate complex models effectively.

The workflow helps assess models for various qualities.

Users can select the most relevant model for their task.

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.