
OCTree is a novel framework that leverages large language models (LLMs) and decision tree reasoning to automate the generation of effective features for tabular data, improving the performance of various prediction models.


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OCTree: Using LLMs and Decision Tree Reasoning for Automated Feature Generation in Tabular Data


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SMART, a novel approach leveraging semantic technologies and reinforcement learning, automates the generation of interpretable features, improving both the accuracy and understandability of machine learning models.


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Semantic-Guided Reinforcement Learning (SMART) for Interpretable Feature Engineering: Enhancing Machine Learning Model Performance and Interpretability



Automated feature engineering can improve downstream predictive performance by automatically creating new features that capture complex interactions between existing features. The proposed IIFE algorithm uses interaction information to efficiently identify and combine feature pairs that synergize well in predicting the target.


interaction-information-based-automated-feature-engineering-for-improved-predictive-performance


Interaction Information Based Automated Feature Engineering for Improved Predictive Performance
