# A-PETE: Adaptive Prototype Explanations of Tree Ensembles

arXiv: https://arxiv.org/abs/2405.21036 · HTML: https://mlsanity.com/paper/2405.21036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 1 of 1)

- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)

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

- [This part looks alike this: identifying important parts of explained instances and prototypes](https://mlsanity.com/paper/2505.05597/) (2025)
- [An interpretable prototype parts-based neural network for medical tabular data](https://mlsanity.com/paper/2603.05423/) (2026)
- [Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations](https://mlsanity.com/paper/2605.21646/) (2026)

## Abstract (arXiv)

The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection of prototypes for these classifiers. Its unique characteristics is using a specialised distance measure and a modified k-medoid approach. Experiments demonstrated its competitive predictive accuracy with respect to earlier explanation algorithms. It also provides a a sufficient number of prototypes for the purpose of interpreting the random forest classifier.