Treemap

Treemaps are effective for visualizing large, hierarchical datasets, offering a clear view of the relative sizes of different categories. The area of each rectangle is proportional to the quantity it represents, allowing for easy comparison of values across the hierarchy. Colors can be used to represent an additional dimension of the data. Treemaps are commonly used in AI to visualize model performance across different data slices, represent feature importance in decision trees, or display the composition of large datasets used for training. Because of their ability to show a part-to-whole relationship effectively, treemaps help provide intuitive insights into complex data structures that AI models process. Some limitations are that treemaps aren't suited for exact value readings and can be difficult to interpret when the hierarchy has many levels or the rectangles have extreme aspect ratios.