# Discovering Interpretable Machine Learning Models in Parallel Coordinates

arXiv: https://arxiv.org/abs/2106.07474 · HTML: https://mlsanity.com/paper/2106.07474/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 14 Jun 2021.

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

- [Survey of explainable machine learning with visual and granular methods beyond quasi-explanations](https://mlsanity.com/paper/2009.10221/) (2020)

## Built on by (most cited first, 7 of 7)

- [Visual Knowledge Discovery with General Line Coordinates](https://mlsanity.com/paper/2305.18429/) (2023)
- [Interpretable Machine Learning for Self-Service High-Risk Decision-Making](https://mlsanity.com/paper/2205.04032/) (2022)
- [Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery](https://mlsanity.com/paper/2206.06476/) (2022)
- [Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling](https://mlsanity.com/paper/2305.18432/) (2023)
- [Parallel Coordinates for Discovery of Interpretable Machine Learning Models](https://mlsanity.com/paper/2305.18434/) (2023)
- [General Line Coordinates in 3D](https://mlsanity.com/paper/2403.13014/) (2024)
- [Fully Explainable Classification Models Using Hyperblocks](https://mlsanity.com/paper/2506.06986/) (2025)

## Abstract (arXiv)

This paper contributes to interpretable machine learning via visual knowledge discovery in parallel coordinates. The concepts of hypercubes and hyper-blocks are used as easily understandable by end-users in the visual form in parallel coordinates. The Hyper algorithm for classification with mixed and pure hyper-blocks (HBs) is proposed to discover hyper-blocks interactively and automatically in individual, multiple, overlapping, and non-overlapping setting. The combination of hyper-blocks with linguistic description of visual patterns is presented too. It is shown that Hyper models generalize decision trees. The Hyper algorithm was tested on the benchmark data from UCI ML repository. It allowed discovering pure and mixed HBs with all data and then with 10-fold cross validation. The links between hyper-blocks, dimension reduction and visualization are established. Major benefits of hyper-block technology and the Hyper algorithm are in their ability to discover and observe hyper-blocks by end-users including side by side visualizations making patterns visible for all classes. Another advantage of sets of HBs relative to the decision trees is the ability to avoid both data overgeneralization and overfitting.