A toolkit for easily building and evaluating machine learning models.
R tutorial: https://ccs-lab.github.io/easyml/
You can install the latest development version from github with:
if (packageVersion("devtools") < 1.6) {
install.packages("devtools")
}
devtools::install_github("CCS-Lab/easyml", subdir = "R")
If you encounter a clear bug, please file a minimal reproducible example on github.
Load the easyml
library:
library(easyml)
For a dataset with a continuous dependent variable:
data("prostate", package = "easyml")
results <- easy_glmnet(prostate, "lpsa")
For a dataset with a binary dependent variable:
data("cocaine_dependence", package = "easyml")
results <- easy_glmnet(cocaine_dependence, "diagnosis",
family = "binomial", exclude_variables = c("subject", "age"),
categorical_variables = c("male"))
A whitepaper for easyml is available at https://doi.org/10.1101/137240. If you find this code useful please cite us in your work:
@article {Hendricks137240,
author = {Hendricks, Paul and Ahn, Woo-Young},
title = {Easyml: Easily Build And Evaluate Machine Learning Models},
year = {2017},
doi = {10.1101/137240},
publisher = {Cold Spring Harbor Labs Journals},
URL = {http://biorxiv.org/content/early/2017/05/12/137240},
journal = {bioRxiv}
}
Hendricks, P., & Ahn, W.-Y. (2017). Easyml: Easily Build And Evaluate Machine Learning Models. bioRxiv, 137240. http://doi.org/10.1101/137240
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