# Interactive slice visualization for exploring machine learning models

arXiv: https://arxiv.org/abs/2101.06986 · HTML: https://mlsanity.com/paper/2101.06986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [The Grammar of Interactive Explanatory Model Analysis](https://mlsanity.com/paper/2005.00497/) (2020)
- [Explanations of model predictions with live and breakDown packages](https://mlsanity.com/paper/1804.01955/) (2018)
- [VINE: Visualizing Statistical Interactions in Black Box Models](https://mlsanity.com/paper/1904.00561/) (2019)
- [A slice tour for finding hollowness in high-dimensional data](https://mlsanity.com/paper/1910.10854/) (2019)

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

- [Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning](https://mlsanity.com/paper/2407.10580/) (2024)

## Abstract (arXiv)

Machine learning models fit complex algorithms to arbitrarily large datasets. These algorithms are well-known to be high on performance and low on interpretability. We use interactive visualization of slices of predictor space to address the interpretability deficit; in effect opening up the black-box of machine learning algorithms, for the purpose of interrogating, explaining, validating and comparing model fits. Slices are specified directly through interaction, or using various touring algorithms designed to visit high-occupancy sections or regions where the model fits have interesting properties. The methods presented here are implemented in the R package pkgcondvis2.