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 model 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.

通过系统审视 259 篇近 10 年来发表以及 2010 年前的代表性著作，我们构建了一个分类法，其中包括技术建模前、建模过程中以及建模后的三个一级类别；每个类别可以进一步分为具体的分析任务，并列举了一些最近具有影响力的作品。同时，我们还讨论和突出了有挑战和有前景的研究机会，这些对于视觉分析的研究者非常有用。