Featpilot: Automatic Feature Augmentation on Tabular Data

Tabular data within enterprises or open data repositories provide a huge opportunity for feature augmentation. Using these data sources to augment training data often boosts model performance, which is crucial in data-centric AutoML systems. Recent works on automatic feature augmentation have limited capabilities in utilizing useful features that cannot be joined with the base table without connecting through intermediate tables. We present Featpilot, a novel framework that explores and integrates high-quality features in tabular data for ML models. Featpilot evaluates a candidate feature from two aspects: (1) the efficacy of a join path connecting the feature to the base table and (2) the intrinsic value of a feature towards an ML task. Featpilot efficiently identifies high-quality features and their optimized join paths to augment the base table. Our experimental results show that Featpilot achieves up to a 10.27% improvement in ML model performance compared to state-of-the-art solutions across six public datasets.