A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.
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README.md

scikit-learn benchmarks

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A centralized repository to report scikit-learn model performance across a variety of parameter settings and datasets.

Downloading the benchmark data

Please refer to PMLB to gain access to the curated datasets from this study. PMLB provides an easy-to-use Python interface to download the datasets.

Contributing

We welcome you to check the existing issues for bugs or enhancements to work on. If you have an idea for an extension of this project, please file a new issue so we can discuss it. Make sure to review our contribution guidelines before starting any work on this project.

Citing

If you use any of the code, data, or results from this project, please cite the following paper.

Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore (2017). Data-driven Advice for Applying Machine Learning to Bioinformatics Problems. arXiv e-print

BibTeX entry:

@misc{OlsonLaCava2017,
    author={Olson, Randal S. and La Cava, William and Mustahsan, Zairah and Varik, Akshay and Moore, Jason H.},
    title = {Data-driven Advice for Applying Machine Learning to Bioinformatics Problems},
    year = {2017},
    howpublished = {arXiv e-print. https://arxiv.org/abs/1708.05070},
}

Support for this project

This project was developed in the Computational Genetics Lab with funding from the NIH. We're incredibly grateful for their support during the development of this project!