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Autofeat

Autofeat is a Python library that provides sklearn-compatible linear prediction models with automated feature engineering and selection capabilities.

Overview

Autofeat simplifies the process of improving linear model performance by automating feature generation and selection. It first generates a wide range of non-linear features, then selects a small, robust subset of meaningful features that enhance the predictive power of linear models. This multi-step approach allows you to harness the interpretability of linear models without sacrificing accuracy.

Key Features:

Use Cases:

Note: The code is intended for research purposes. Results may vary depending on the dataset and use case.

Installation

Autofeat is available on PyPI, making it easy to install via pip:

pip install autofeat

Other Dependencies

Documentation and Resources

DescriptionLink
Example Notebooksexamples
Documentationdocumentation
Paperpaper
TalkPyData talk

If any of this code was helpful for your work, please consider citing the paper:

@inproceedings{horn2019autofeat,
  title={The autofeat Python Library for Automated Feature Engineering and Selection},
  author={Horn, Franziska and Pack, Robert and Rieger, Michael},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={111--120},
  year={2019},
  organization={Springer}
}

If you have any questions please don't hesitate to send me an email and of course if you should find any bugs or want to contribute other improvements, pull requests are very welcome!

Acknowledgments

This project was made possible thanks to support by BASF.