This paper introduces GPT-HTree, a framework combining hierarchical clustering, decision trees, and large language models (LLMs) to address this challenge. By leveraging hierarchical clustering to segment individuals based on salient features, resampling techniques to balance class distributions, and decision trees to tailor classification paths within each cluster, GPT-HTree ensures both accuracy and interpretability. LLMs enhance the framework by generating human-readable cluster descriptions, bridging quantitative analysis with actionable insights.
@article{arxiv.2501.13743,
title = {GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification},
author = {Te Pei and Fuat Alican and Aaron Ontoyin Yin and Yigit Ihlamur},
journal= {arXiv preprint arXiv:2501.13743},
year = {2025}
}