Conceptual Views on Tree Ensemble Classifiers

TL;DR

This paper introduces an algebraic, lattice-theory-based approach for providing global explanations of tree ensemble classifiers, addressing the explainability limitations of existing statistical methods.

Contribution

It proposes two novel conceptual views on tree ensemble classifiers that enhance their interpretability through algebraic explanations.

Findings

Demonstrates the explanatory capabilities on Random Forests trained with standard parameters

Introduces two new conceptual views rooted in lattice theory

Addresses the explainability gap in tree ensemble methods

Abstract

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.