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.
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
Bibtex:
@inproceedings{la_cava_learning_2019,
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},
}
This method is being developed to study human disease in the Epistasis Lab at UPenn.
GNU GPLv3