A toolkit for easily building and evaluating machine learning models.
See installation instructions for the Python or R packages.
If you encounter a clear bug, please file a minimal reproducible example on github.
A whitepaper for easyml is available at http://arxiv.org/abs/TOBEEDITED. If you find this code useful please cite us in your work:
@inproceedings{TOBEEDITED,
title = {easyml: A toolkit for easily building and evaluating machine learning models},
author = {Paul Hendricks and Woo-Young Ahn},
eprint = {arXiv:TOBEEDITED},
year = {2017},
}
Ahn, W.-Y.∗, Ramesh∗, D., Moeller, F. G., & Vassileva, J. (2016) Utility of machine learning approaches to identify behavioral markers for substance use disorders: Impulsivity dimensions as predictors of current cocaine dependence. Frontiers in Psychiatry, 7: 34. PDF ∗Co-first authors
Ahn, W.-Y. & Vassileva, J. (2016) Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug and Alcohol Dependence, 161 (1), 247–257. PDF
Ahn, W.-Y., Kishida, K. T., Gu, X., Lohrenz, T., Harvey, A. H., Alford, J. R., Smith, K. B., Yaffe, G., Hibbing, J. R., Dayan, P., & Montague, P. R. (2014) Nonpolitical images evoke neural predictors of political ideology. Current Biology, 24(22), 2693-2599. PDF SOM