Gaggle: Visual Analytics for Model Space Navigation

Subhajit Das, Dylan Cashman, Remco Chang, Alex Endert

Abstract: Recent visual analytics systems make use of multiple machine learning models to better fit the data as opposed to traditional single, pre-defined model systems. However, while multi-model visual analytic systems can be effective, their added complexity poses usability concerns, as users are required to interact with the parameters of multiple models. Further, the advent of various model algorithms and associated hyperparameters creates an exhaustive model space to sample models from. This poses complexity to navigate this model space to find the right model for the data and the task. In this paper, we present Gaggle, a multi-model visual analytic system that enables users to interactively navigate the model space. Further translating user interactions into inferences, Gaggle simplifies working with multiple models by automatically finding the best model from the high-dimensional model space to support various user tasks. Through a qualitative user study, we show how our approach helps users to find a best model for a classification and ranking task. The study results confirm that Gaggle is intuitive and easy to use, supporting interactive model space navigation and automated model selection without requiring any technical expertise from users.

Keywords: visual analytics, interactive machine learning, classification, ranking

TL;DR: Gaggle, an interactive visual analytic system to help users interactively navigate model space for classification and ranking tasks.