# VINE: Visualizing Statistical Interactions in Black Box Models

arXiv: https://arxiv.org/abs/1904.00561 · HTML: https://mlsanity.com/paper/1904.00561/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 13 of 13)

- [Explaining Hyperparameter Optimization via Partial Dependence Plots](https://mlsanity.com/paper/2111.04820/) (2021)
- [Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment](https://mlsanity.com/paper/2202.02838/) (2022)
- [REPID: Regional Effect Plots with implicit Interaction Detection](https://mlsanity.com/paper/2202.07254/) (2022)
- [RHALE: Robust and Heterogeneity-aware Accumulated Local Effects](https://mlsanity.com/paper/2309.11193/) (2023)
- [Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots](https://mlsanity.com/paper/2404.18702/) (2024)
- [iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios](https://mlsanity.com/paper/2306.07775/) (2023)
- [CohEx: A Generalized Framework for Cohort Explanation](https://mlsanity.com/paper/2410.13190/) (2024)
- [FINCH: Locally Visualizing Higher-Order Feature Interactions in Black Box Models](https://mlsanity.com/paper/2503.16445/) (2025)
- [Interactive slice visualization for exploring machine learning models](https://mlsanity.com/paper/2101.06986/) (2021)
- [Effector: A Python package for regional explanations](https://mlsanity.com/paper/2404.02629/) (2024)
- [GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations](https://mlsanity.com/paper/2601.22771/) (2026)
- [Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation](https://mlsanity.com/paper/2410.14082/) (2024)
- [Implet: A Post-hoc Subsequence Explainer for Time Series Models](https://mlsanity.com/paper/2505.08748/) (2025)

## Abstract (arXiv)

As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional explanations - how groups of similar instances behave in a complex model, and the related issue of visualizing statistical feature interactions. The lack of utilities available for these analytical needs hinders the development of models that are mission-critical, transparent, and align with social goals. We present VINE (Visual INteraction Effects), a novel algorithm to extract and visualize statistical interaction effects in black box models. We also present a novel evaluation metric for visualizations in the interpretable ML space.