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 with 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 on 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.
Original language | English |
---|---|
Title of host publication | Machine Learning for Data Science Handbook |
Subtitle of host publication | Data Mining and Knowledge Discovery Handbook, Third Edition |
Publisher | Springer International Publishing |
Pages | 945-970 |
Number of pages | 26 |
ISBN (Electronic) | 9783031246289 |
ISBN (Print) | 9783031246272 |
DOIs | |
State | Published - 1 Jan 2023 |