A survey of visual analytics techniques for machine learning

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.

Automated summary

This study systematically reviews 259 papers published in the last decade and representative works before 2010 in the field of visual analytics for machine learning. It establishes a taxonomy with three main categories: techniques before, during, and after model building, each characterized by various analysis tasks and influential works. The study also discusses research challenges and potential future opportunities for visual analytics researchers.