# Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models

arXiv: https://arxiv.org/abs/2205.04035 · HTML: https://mlsanity.com/paper/2205.04035/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 1 of 1)

- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)

## Built on by (most cited first, 3 of 3)

- [Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling](https://mlsanity.com/paper/2305.18432/) (2023)
- [Cluster-Based Random Forest Visualization and Interpretation](https://mlsanity.com/paper/2507.22665/) (2025)
- [General Line Coordinates in 3D](https://mlsanity.com/paper/2403.13014/) (2024)

## Abstract (arXiv)

Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. The new method expands and complements the capabilities of existing methods, to visualize DT models. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space. This information is important for domain experts for evaluating and improving the DT models, including avoiding overgeneralization and overfitting of models, along with their performance. The benefits of the methods are demonstrated in the case studies, using three real datasets.