# Generalized SHAP: Generating multiple types of explanations in machine learning

arXiv: https://arxiv.org/abs/2006.07155 · HTML: https://mlsanity.com/paper/2006.07155/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Ensembles of Random SHAPs](https://mlsanity.com/paper/2103.03302/) (2021)
- [An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data](https://mlsanity.com/paper/2106.09111/) (2021)
- [Glocal Explanations of Expected Goal Models in Soccer](https://mlsanity.com/paper/2308.15559/) (2023)
- [Explaining Probabilistic Models with Distributional Values](https://mlsanity.com/paper/2402.09947/) (2024)
- [Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring](https://mlsanity.com/paper/2212.05866/) (2022)
- [A New Technique for AI Explainability using Feature Association Map](https://mlsanity.com/paper/2605.12350/) (2026)

## Abstract (arXiv)

Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output. To answer a broader set of questions, we generalize a popular, mathematically well-grounded explanation technique, Shapley Additive Explanations (SHAP). Our new method - Generalized Shapley Additive Explanations (G-SHAP) - produces many additional types of explanations, including: 1) General classification explanations; Why is this sample more likely to belong to one class rather than another? 2) Intergroup differences; Why do our model's predictions differ between groups of observations? 3) Model failure; Why does our model perform poorly on a given sample? We formally define these types of explanations and illustrate their practical use on real data.