# Using Visual Analytics to Interpret Predictive Machine Learning Models

arXiv: https://arxiv.org/abs/1606.05685 · HTML: https://mlsanity.com/paper/1606.05685/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2016.

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

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [i-Algebra: Towards Interactive Interpretability of Deep Neural Networks](https://mlsanity.com/paper/2101.09301/) (2021)

## Abstract (arXiv)

It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice.