Integrating Language Models for Automating Feature Engineering Ideation

Feature engineering has long relied on manual expertise, demanding domain knowledge and experience. While automated approaches exist, they often involve brute-force methods and subsequent feature selection. The emergence of Large Language Models (LLMs) offers a novel perspective on feature engineering. LLMs encapsulate extensive knowledge, presenting an opportunity to reshape this process.

In this presentation, we explore an approach that utilizes LLMs to guide feature engineering. By leveraging the contextual understanding within LLMs, we have developed a system for LLM-assisted feature engineering. Our research demonstrates the practical benefits of this synergy – from feature ideation to improved feature relevance to enhanced model interpretability and efficiency.

In this presentation by Head of Semantic Data Science Sergey Yurgenson, discover how our framework automates and enhances feature engineering, contributing to better predictive models. This talk showcases the integration of human expertise with language models, revolutionizing feature engineering in data science.