# Visual Analytics and Human Involvement in Machine Learning

arXiv: https://arxiv.org/abs/2005.06057 · HTML: https://mlsanity.com/paper/2005.06057/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 3 of 3)

- [Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions](https://mlsanity.com/paper/2205.01296/) (2022)
- [SFS-A68: a dataset for the segmentation of space functions in apartment buildings](https://mlsanity.com/paper/2209.09094/) (2022)
- [SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings](https://mlsanity.com/paper/2307.04515/) (2023)

## Abstract (arXiv)

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.