  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.
