# Full interpretable machine learning in 2D with inline coordinates

arXiv: https://arxiv.org/abs/2106.07568 · HTML: https://mlsanity.com/paper/2106.07568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [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, 4 of 4)

- [Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions](https://mlsanity.com/paper/2205.01296/) (2022)
- [Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling](https://mlsanity.com/paper/2305.18432/) (2023)
- [Deep Learning Image Recognition for Non-images](https://mlsanity.com/paper/2106.14350/) (2021)
- [Full High-Dimensional Intelligible Learning In 2-D Lossless Visualization Space](https://mlsanity.com/paper/2305.19132/) (2023)

## Abstract (arXiv)

This paper proposed a new methodology for machine learning in 2-dimensional space (2-D ML) in inline coordinates. It is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. It allows discovering n-D patterns in 2-D space without loss of n-D information using graph representations of n-D data in 2-D. Specifically, it can be done with the inline based coordinates in different modifications, including static and dynamic ones. The classification and regression algorithms based on these inline coordinates were introduced. A successful case study based on a benchmark data demonstrated the feasibility of the approach. This approach helps to consolidate further a whole new area of full 2-D machine learning as a promising ML methodology. It has advantages of abilities to involve actively the end-users into the discovering of models and their justification. Another advantage is providing interpretable ML models.