Vis Network Decision Tree
Interactive decision tree drawn with vis-network's hierarchical UD layout. Internal nodes carry split conditions; leaf nodes carry the predicted class with a colour-coded fill. Useful for ML model explainability, customer-segmentation rule explorers, and rule-of-thumb diagrams.
Template details
- chart
- network
- graph
- visjs
- vis-network
- decision-tree
- ml
- hierarchy
When to use this template
This visualization helps analysts understand the sequential conditions or rules that lead to specific outcomes. It is ideal for dissecting complex decision-making processes, interpreting machine learning models, or exploring how different factors combine to segment a population into distinct groups.
- Datasets with a clear hierarchical or tree-like structure.
- Features that can be split into discrete conditions (categorical or binned numerical).
- A single, well-defined target variable (e.g., class label, outcome group).
- Moderate tree depth (e.g., 3-7 levels) for optimal readability.
- Data where the path or sequence of conditions to an outcome is critical.
- Rule sets derived from classification or regression tree models.