b21e658a02
* [R-package] JSON tree dump interface * [R-package] precision bugfix in xgb.attributes * [R-package] bugfix for cb.early.stop called from xgb.cv * [R-package] a bit more clarity on labels checking in xgb.cv * [R-package] test JSON dump for gblinear as well * whitespace lint
XGBoost R Package for Scalable GBM
Resources
- XGBoost R Package Online Documentation
- Check this out for detailed documents, examples and tutorials.
Installation
We are on CRAN now. For stable/pre-compiled(for Windows and OS X) version, please install from CRAN:
install.packages('xgboost')
You can also install from our weekly updated drat repo:
install.packages("xgboost", repos=c("http://dmlc.ml/drat/", getOption("repos")), type="source")
Important Due to the usage of submodule, install_github is no longer support to install the
latest version of R package.
For up-to-date version, please install from github.
Windows users will need to install RTools first. They also need to download MinGW-W64 using x86_64 architecture during installation.
Run the following command to add MinGW to PATH in Windows if not already added.
PATH %PATH%;C:\Program Files\mingw-w64\x86_64-5.3.0-posix-seh-rt_v4-rev0\mingw64\bin
To compile xgboost at the root of your storage, run the following bash script.
git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
git submodule init
git submodule update
alias make='mingw32-make'
cd dmlc-core
make -j4
cd ../rabit
make lib/librabit_empty.a -j4
cd ..
cp make/mingw64.mk config.mk
make -j4
Run the following R script to install xgboost package from the root directory.
install.package('devtools') # if not installed
setwd('C:/xgboost/')
library(devtools)
install('R-package')
For more detailed installation instructions, please see here.
Examples
- Please visit walk through example.
- See also the example scripts for Kaggle Higgs Challenge, including speedtest script on this dataset and the one related to Otto challenge, including a RMarkdown documentation.