# Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling

arXiv: https://arxiv.org/abs/2305.18432 · HTML: https://mlsanity.com/paper/2305.18432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 10 of 10)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Exploring the Whole Rashomon Set of Sparse Decision Trees](https://mlsanity.com/paper/2209.08040/) (2022)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization](https://mlsanity.com/paper/2209.09227/) (2022)
- [Discovering Interpretable Machine Learning Models in Parallel Coordinates](https://mlsanity.com/paper/2106.07474/) (2021)
- [Full interpretable machine learning in 2D with inline coordinates](https://mlsanity.com/paper/2106.07568/) (2021)
- [Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates](https://mlsanity.com/paper/2107.04974/) (2021)
- [Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning](https://mlsanity.com/paper/2107.04971/) (2021)
- [Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions](https://mlsanity.com/paper/2205.01296/) (2022)
- [Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models](https://mlsanity.com/paper/2205.04035/) (2022)

## Built on by (most cited first, 1 of 1)

- [High-Dimensional Data Classification in Concentric Coordinates](https://mlsanity.com/paper/2507.18450/) (2025)

## Abstract (arXiv)

To increase the interpretability and prediction accuracy of the Machine Learning (ML) models, visualization of ML models is a key part of the ML process. Decision Trees (DTs) are essential in machine learning (ML) because they are used to understand many black box ML models including Deep Learning models. In this research, two new methods for creation and enhancement with complete visualizing Decision Trees as understandable models are suggested. These methods use two versions of General Line Coordinates (GLC): Bended Coordinates (BC) and Shifted Paired Coordinates (SPC). The Bended Coordinates are a set of line coordinates, where each coordinate is bended in a threshold point of the respective DT node. In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. These new methods expand and complement the capabilities of existing methods to visualize DT models more completely. These capabilities allow us to observe and analyze: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) sensitivity of each split threshold in the DT nodes, and (5) density of cases in parts of the n-D space. These features are critical for DT models' performance evaluation and improvement by domain experts and end users as they help to prevent overgeneralization and overfitting of the models. The advantages of this methodology are illustrated in the case studies on benchmark real-world datasets. The paper also demonstrates how to generalize them for decision tree visualizations in different General Line Coordinates.