A Haskell wrapper for DMLC's xgboost machine learning library.
Xgboost is an optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
It implements machine learning algorithms under the Gradient Boosting framework, including Generalized Linear Model (GLM) and Gradient Boosted Decision Trees (GBDT). XGBoost can also be distributed and scale to Terascale data.
To get started, run the standard:
cabal sandbox init
cabal configure
cabal build
cabal install
You can then enter GHCI with the sandboxed packages using
# Assuming GHC >= 7.6
# Note we have to link the dynamic xgboostwrapper library manually when using ghci: http://stackoverflow.com/questions/6323755/osx-ghci-dylib-what-is-the-correct-way
ghci -no-user-package-db -package-db .cabal-sandbox/*.conf.d xgboost/wrapper/libxgboost.so
This project is licensed under the MIT License:
Copyright (c) 2015-2016 Robert Krzyzanowski
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
This wrapper library was created by Robert Krzyzanowski, technoguyrob@gmail.com. The underlying library was designed by Tianqi Chen and his team at DMLC (Distributed (Deep) Machine Learning Common).