Visual Analytics and Human Involvement in Machine Learning

TL;DR

This paper reviews how visual analytics support human decision-making throughout the machine learning process, emphasizing the importance of visualization choices tailored to data, models, and steps.

Contribution

It provides a comprehensive overview of visualization techniques aligned with each step of the ML process, highlighting their roles in human-in-the-loop analytics.

Findings

Different visualizations are suited for different ML steps.

Visualization choices depend on data type, model, and purpose.

Visual analytics enhance human understanding and decision-making in ML.

Abstract

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.