Visual Analytics and Human Involvement in Machine Learning
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2020.
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.
- 2022Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directionscited by 3
- 2022SFS-A68: a dataset for the segmentation of space functions in apartment buildingscited by 1
- 2023SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildingscited by 0
- 2023SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings10 Jul 2023
- 2022SFS-A68: a dataset for the segmentation of space functions in apartment buildings13 Sep 2022
- 2022Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions3 May 2022