arXiv:2607.01548v1 Announce Type: new Abstract: Large language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data. EFE represents transformations as Python programs with a standardized fit/transform interface, allowing them to be inserted directly into existing machine learning pipelines. During evolution, candidate programs are refined using dataset context, summary statistics, and downstream perform
The increasing sophistication of large language models and their application in optimization problems is driving new approaches to automating complex data science tasks like feature engineering.
Automating feature engineering using LLMs can significantly reduce the lead time and expertise required for developing high-performing machine learning models, democratizing advanced AI capabilities.
Machine learning pipelines will become more autonomous and efficient in data preprocessing, potentially enabling faster iteration and deployment of AI solutions across industries.
- · AI/ML developers
- · Data scientists
- · Businesses leveraging AI
- · Cloud AI platforms
- · Manual feature engineering specialists
- · Legacy data preprocessing tools
LLMs gain a new, powerful application in optimizing critical stages of the machine learning lifecycle.
The efficiency gains from LLM-driven feature engineering accelerate the development and deployment of AI agents and autonomous systems.
Increased accessibility to advanced AI model development could intensify competition and innovation in sectors heavily reliant on data-driven decision making.
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Read at arXiv cs.LG