LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers

ArXi:2503.14434v3 Announce Type: replace-cross Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limited by their reliance on pre-defined transformations within fixed, manually designed search spaces, often neglecting domain knowledge. Recent advances using Large Language Models (LLMs) have enabled the integration of domain knowledge into the feature engineering process.