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Easyml: Easily Build and Evaluate Machine Learning Models

Woo-Young Ahn, Paul Hendricks, Nathaniel Haines
doi: https://doi.org/10.1101/137240
Woo-Young Ahn
Department of Psychology Seoul National University Seoul, Korea 08826
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Paul Hendricks
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Nathaniel Haines
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Abstract

The easyml (easy machine learning) package lowers the barrier to entry to machine learning and is ideal for undergraduate/graduate students, and practitioners who want to quickly apply machine learning algorithms to their research without having to worry about the best practices of implementing each algorithm. The package provides standardized recipes for regression and classification algorithms in R and Python and implements them in a functional, modular, and extensible framework. This package currently implements recipes for several common machine learning algorithms (e.g., penalized linear models, random forests, and support vector machines) and provides a unified interface to each one. Importantly, users can run and evaluate each machine learning algorithm with a single line of coding. Each recipe is robust, implements best practices specific to each algorithm, and generates a report with details about the model, its performance, as well as journal-quality visualizations. The package’s functional, modular, and extensible framework also allows researchers and more advanced users to easily implement new recipes for other algorithms.

Footnotes

  • wahn55{at}snu.ac.kr, hendricks.201{at}osu.edu, haines.175{at}osu.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 31, 2017.
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Easyml: Easily Build and Evaluate Machine Learning Models
Woo-Young Ahn, Paul Hendricks, Nathaniel Haines
bioRxiv 137240; doi: https://doi.org/10.1101/137240
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Easyml: Easily Build and Evaluate Machine Learning Models
Woo-Young Ahn, Paul Hendricks, Nathaniel Haines
bioRxiv 137240; doi: https://doi.org/10.1101/137240

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