# GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification

arXiv: https://arxiv.org/abs/2501.13743 · HTML: https://mlsanity.com/paper/2501.13743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2025.

## Abstract (arXiv)

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.