Feature Engineering Automation Tool

Build Status License: GPL v3

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.

FEAT uses the Shogun C++ ML toolbox to fit models.

Check out the documentation for installation and examples.


  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


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


Special thanks to these contributors:

  • Tilak Raj Singh @tilakhere

  • Srinivas Suri @srinu634

  • James P Taggert @JPT2

  • Daniel Herman

  • Paul Lee

This work is supported by grant K99-LM012926 and R00-LM012926 from the National Library of Medicine. FEAT is being developed to learn clinical diagnostics in the Cava Lab at Harvard Medical School .



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