# Interpretable Visualizations of Data Spaces for Classification Problems

arXiv: https://arxiv.org/abs/2503.05861 · HTML: https://mlsanity.com/paper/2503.05861/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 63 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Unsupervised and Supervised Principal Component Analysis: Tutorial](https://mlsanity.com/paper/1906.03148/) (2019)
- [Improving Sample and Feature Selection with Principal Covariates Regression](https://mlsanity.com/paper/2012.12253/) (2020)
- [Structure-Property Maps with Kernel Principal Covariates Regression](https://mlsanity.com/paper/2002.05076/) (2020)
- [A data-driven interpretation of the stability of molecular crystals](https://mlsanity.com/paper/2209.10709/) (2022)

## Abstract (arXiv)

How do classification models "see" our data? Based on their success in delineating behaviors, there must be some lens through which it is easy to see the boundary between classes; however, our current set of visualization techniques makes this prospect difficult. In this work, we propose a hybrid supervised-unsupervised technique distinctly suited to visualizing the decision boundaries determined by classification problems. This method provides a human-interpretable map that can be analyzed qualitatively and quantitatively, which we demonstrate through visualizing and interpreting a decision boundary for chemical neurotoxicity. While we discuss this method in the context of chemistry-driven problems, its application can be generalized across subfields for "unboxing" the operations of machine-learning classification models.