A User-based Visual Analytics Workflow for Exploratory Model Analysis
This work addresses the need for users to produce accurate predictive models for future use, rather than just gaining insights, but it is incremental as it builds on existing visual analytics concepts.
The paper tackles the problem of helping users generate and select predictive models through a visual analytics workflow for Exploratory Model Analysis (EMA), finding that the system enabled users to create complex models, assess their qualities, and choose the most relevant one for their tasks.
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.