Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

机器学习的可视化分析最近发展成为可视化领域中最令人兴奋的领域之一。为了更好地确定哪些研究主题是有前途的，并学习如何在视觉分析中应用相关技术，我们系统地回顾了过去十年中发表的259篇论文以及2010年之前的代表性作品。 ：模型构建之前的技术，模型构建期间的技术以及模型构建之后的技术。每个类别的特征还在于具有代表性的分析任务，并且每个任务都由一组近期的有影响力的作品来举例说明。我们还将讨论并重点介绍对视觉分析研究人员有用的研究挑战和潜在的潜在未来研究机会。