ClaVis: An Interactive Visual Comparison System for Classifiers

Frank Heyen, Tanja Munz, Michael Neumann, Daniel Ortega, Ngoc Thang Vu, Daniel Weiskopf, Michael Sedlmair

Abstract: We propose ClaVis, a visual analytics system for comparative analysis of classification models. ClaVis allows users to visually compare the performance and behavior of tens to hundreds of classifiers trained with different hyperparameter configurations. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that reveals correlations between parameters and scores, and a training history chart. We demonstrate the effectivity of our approach in multiple case studies for training classification models in the domain of natural language processing.