XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
XGBoost可在Windows、Linux、Mac OS X上使用，支持多种编程语言：C++, Python, R, Java, Scala, Julia等。
安装XGBoost-Python
安装基本开发工具：
$ sudo apt install git build-essential python-dev python-setuptools python-pip python-numpy python-scipy
从Github下载最新源代码：
$ git clone --recursive https://github.com/dmlc/xgboost
编译：
$ cd xgboost
$ make -j4
生成的库：lib/libxgboost.so、lib/libxgboost.a；命令行工具：xgboost。
安装Python包：
$ cd python-package/
$ sudo python setup.py install
# 或
# $ sudo python setup.py install --user
测试：
import xgboost as xgb
# read in data
dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
# specify parameters via map
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
num_round = 2
bst = xgb.train(param, dtrain, num_round)
# make prediction
preds = bst.predict(dtest)
安装XGBoost-R
安装XGBoost包：
> install.packages('xgboost')
> library(xgboost)
测试：
# load data
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
# fit model
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2, nthread = 2, objective = "binary:logistic")
# predict
pred <- predict(bst, test$data)
XGBoost源代码：https://github.com/dmlc/xgboost
XGBoost的文档：http://xgboost.readthedocs.io/en/latest/