Learning Ensembles of Interpretable Simple Structure

TL;DR

This paper introduces a bottom-up algorithm that identifies simple, interpretable structures within complex data by partitioning data into subgroups, enhancing both interpretability and accuracy of models in decision-making tasks.

Contribution

It presents a novel method for discovering simple, interpretable structures within data, improving transparency and performance over traditional global models.

Findings

The algorithm effectively identifies simple structures in synthetic data.

Decision boundaries from simple structures are more interpretable.

The approach enhances both explainability and predictive accuracy.

Abstract

Decision-making in complex systems often relies on machine learning models, yet highly accurate models such as XGBoost and neural networks can obscure the reasoning behind their predictions. In operations research applications, understanding how a decision is made is often as crucial as the decision itself. Traditional interpretable models, such as decision trees and logistic regression, provide transparency but may struggle with datasets containing intricate feature interactions. However, complexity in decision-making stem from interactions that are only relevant within certain subsets of data. Within these subsets, feature interactions may be simplified, forming simple structures where simple interpretable models can perform effectively. We propose a bottom-up simple structure-identifying algorithm that partitions data into interpretable subgroups known as simple structure, where feature interactions are minimized, allowing simple models to be trained within each subgroup. We demonstrate the robustness of the algorithm on synthetic data and show that the decision boundaries derived from simple structures are more interpretable and aligned with the intuition of the domain than those learned from a global model. By improving both explainability and predictive accuracy, our approach provides a principled framework for decision support in applications where model transparency is essential.