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

TL;DR

GPT-HTree is a novel framework that combines hierarchical clustering, decision trees, and large language models to improve the accuracy and interpretability of classification tasks, providing human-readable insights.

Contribution

It introduces a new integrated approach that leverages hierarchical clustering and LLMs within decision trees for explainable classification.

Findings

Enhanced interpretability through human-readable cluster descriptions

Balanced class distributions using resampling techniques

Maintained or improved classification accuracy

Abstract

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.