  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.
