Decision trees are said to be inherently interpretable machine learning models. Still, you won’t be able to interpret much if you need to follow hundreds of splits with the index finger, so hopefully…

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This post explores ideas and techniques for improving decision tree visualizations using NetworkX and Matplotlib. It covers translating decision trees into a NetworkX directed graph, optimizing the layout of the graph, using node colors, shapes, and sizes to represent information, and using edge thickness to represent the flow of data along the tree. The visualizations can help improve understanding of decision trees and potentially enhance their performance.

Towards Better Decision Tree Visualizations

0. Translate the decision tree into a NetworkX directed graph

1. Use a meaningful tree layout and optimize it

3. Use edge thickness to represent the flow of data along the tree

Appendix: code for the training data flow diagrams