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.
@article{arxiv.1702.01226,
title = {Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective},
author = {Shixia Liu and Xiting Wang and Mengchen Liu and Jun Zhu},
journal= {arXiv preprint arXiv:1702.01226},
year = {2017}
}
Comments
This article will be published in Visual Infomatics