# Explainable Models via Compression of Tree Ensembles

arXiv: https://arxiv.org/abs/2206.07904 · HTML: https://mlsanity.com/paper/2206.07904/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Interpreting Blackbox Models via Model Extraction](https://mlsanity.com/paper/1705.08504/) (2017)
- [Born-Again Tree Ensembles](https://mlsanity.com/paper/2003.11132/) (2020)
- [Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach](https://mlsanity.com/paper/1606.09066/) (2016)
- [Interpreting Models via Single Tree Approximation](https://mlsanity.com/paper/1610.09036/) (2016)

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

- [Knowledge-based Refinement of Scientific Publication Knowledge Graphs](https://mlsanity.com/paper/2309.05681/) (2023)
- [Pruning-Based Extraction of Descriptions from Probabilistic Circuits](https://mlsanity.com/paper/2311.13379/) (2023)
- [Symbolic Snapshot Ensembles](https://mlsanity.com/paper/2510.24633/) (2025)

## Abstract (arXiv)

Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of PLMs -- interpretability. In this paper we consider the problem of compressing a large set of learned trees into a single explainable model. To this effect, we propose CoTE -- Compression of Tree Ensembles -- that produces a single small decision list as a compressed representation. CoTE first converts the trees to decision lists and then performs the combination and compression with the aid of the original training set. An experimental evaluation demonstrates the effectiveness of CoTE in several benchmark relational data sets.