Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
This is an incremental survey paper that organizes existing research to help researchers and practitioners in machine learning and data mining better navigate and apply visual analytics tools.
The paper tackles the problem of analyzing machine learning models by providing a comprehensive survey and classification of interactive visual analytics methods into understanding, diagnosis, and refinement categories, without presenting new experimental results or concrete numbers.
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.