Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective

Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed. Keywords: interactive model analysis, interactive visualization, machine learning, understanding, diagnosis, refinement 1. Introduction Machine learning has been successfully applied to a wide variety of fields ranging from information retrieval, data mining, and speech recognition, to computer graphics, visualization, and human-computer interaction. However, most users often treat a machine learning model as a black box because of its incomprehensible functions and unclear working mechanism [1, 2, 3]. Without a clear understanding of how and why a model works, the development of highperformance models typically relies on a time-consuming trial-and-error pro-Fully documented templates are available in the elsarticle package on CTAN.