# Conceptual Views on Tree Ensemble Classifiers

arXiv: https://arxiv.org/abs/2302.05270 · HTML: https://mlsanity.com/paper/2302.05270/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [OpenML-Python: an extensible Python API for OpenML](https://mlsanity.com/paper/1911.02490/) (2019)
- [Born-Again Tree Ensembles](https://mlsanity.com/paper/2003.11132/) (2020)
- [On interestingness measures of formal concepts](https://mlsanity.com/paper/1611.02646/) (2016)
- [On the Lattice of Conceptual Measurements](https://mlsanity.com/paper/2012.05267/) (2020)
- [Knowledge Cores in Large Formal Contexts](https://mlsanity.com/paper/2002.11776/) (2020)
- [Relevant Attributes in Formal Contexts](https://mlsanity.com/paper/1812.08868/) (2018)
- [Attribute Selection using Contranominal Scales](https://mlsanity.com/paper/2106.10978/) (2021)
- [On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization](https://mlsanity.com/paper/2202.10669/) (2022)
- [Contextual Decision Trees](https://mlsanity.com/paper/2207.06355/) (2022)
- [Decision Concept Lattice vs. Decision Trees and Random Forests](https://mlsanity.com/paper/2106.00387/) (2021)

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

- [Scaling Dimension](https://mlsanity.com/paper/2302.09101/) (2023)
- [The Geometric Structure of Topic Models](https://mlsanity.com/paper/2403.03607/) (2024)
- [Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2601.10386/) (2026)

## Abstract (arXiv)

Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.