Visual Analytics and Human Involvement in Machine Learning
This work provides a structured overview for data scientists on integrating visual analytics into ML workflows, but it is incremental as it reviews existing techniques without introducing new methods.
The paper reviews visualization techniques used across the seven steps of the machine learning process, highlighting how human decisions rely on visualizations tailored to data properties, models, and analytical purposes.
The rapidly developing AI systems and applications still require human involvement in practically all parts of the analytics process. Human decisions are largely based on visualizations, providing data scientists details of data properties and the results of analytical procedures. Different visualizations are used in the different steps of the Machine Learning (ML) process. The decision which visualization to use depends on factors, such as the data domain, the data model and the step in the ML process. In this chapter, we describe the seven steps in the ML process and review different visualization techniques that are relevant for the different steps for different types of data, models and purposes.