Visual Analytics For Machine Learning: A Data Perspective Survey
Junpeng Wang Shixia Liu Wei Zhang
The past decade has witnessed a plethora of works that leverage the power of
visualization (VIS) to interpret machine learning (ML) models. The
corresponding research topic, VIS4ML, keeps growing at a fast pace. To better
organize the enormous works and shed light on the developing trend of VIS4ML,
we provide a systematic review of these works through this survey. Since data
quality greatly impacts the performance of ML models, our survey focuses
specifically on summarizing VIS4ML works from the data perspective. First, we
categorize the common data handled by ML models into five types, explain the
unique features of each type, and highlight the corresponding ML models that
are good at learning from them. Second, from the large number of VIS4ML works,
we tease out six tasks that operate on these types of data (i.e., data-centric
tasks) at different stages of the ML pipeline to understand, diagnose, and
refine ML models. Lastly, by studying the distribution of 143 surveyed papers
across the five data types, six data-centric tasks, and their intersections, we
analyze the prospective research directions and envision future research
trends.