Feature Engineering Automation Tool

FEAT is a feature engineering automation tool that learns new representations of raw data to improve classifier and regressor performance. The underlying methods use Pareto optimization and evolutionary computation to search the space of possible transformations.

FEAT wraps around a user-chosen ML method and provides a set of representations that give the best performance for that method. Each individual in FEAT’s population is its own data representation.


Maintained by William La Cava (lacava at childrens dot harvard dot edu)


This work is supported by grant R00-LM012926 from the National Library of Medicine. FEAT is being developed to develop predictive health models by the Cavalab .


  1. La Cava, W., Singh, T. R., Taggart, J., Suri, S., & Moore, J. H.. Learning concise representations for regression by evolving networks of trees. ICLR 2019. arxiv:1807.0091

2. La Cava, W. & Moore, Jason H. (2020). Genetic programming approaches to learning fair classifiers. GECCO 2020. Best Paper Award . ACM , arXiv , experiments

3. La Cava, W., Lee, P.C., Ajmal, I., Ding, X., Cohen, J.B., Solanki, P., Moore, J.H., and Herman, D.S (2021). Application of concise machine learning to construct accurate and interpretable EHR computable phenotypes. In Review. medRxiv , experiments

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