Visualization aims at creating a visual representation of data or algorithms. While the amount of available data in multiple domains is growing rapidly, visualization is especially important to provide intuitive access to information hidden in datasets. Therefore, visualization methods can be employed to analyze correlations and patterns in data, and aid in making machine learning models more comprehensible.

A common approach is to use dimensionality reduction techniques to transform high-dimensional datasets to two or three dimensions to allow visualization in (3D) scatterplots. Moreover, (hierrachical) clustering approaches or graph-based approaches can simplify the data by grouping similar instances together.