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.


Maintained by William La Cava (lacava at upenn.edu)


This work is supported by grant K99-LM012926 from the National Library of Medicine. FEAT is being developed to study human disease by the Epistasis Lab at UPenn .


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


    series = {{ICLR}},
    title = {Learning concise representations for regression by evolving networks of trees},
    url = {https://arxiv.org/abs/1807.00981},
    language = {en},
    booktitle = {International {Conference} on {Learning} {Representations}},
    author = {La Cava, William and Singh, Tilak Raj and Taggart, James and Suri, Srinivas and Moore, Jason H.},
    year = {2019},

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