xgboost v0.4-4


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by Tong He

Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of gradient boosting framework. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

Functions in xgboost

Name Description
predict,xgb.Booster-method Predict method for eXtreme Gradient Boosting model
getinfo Get information of an xgb.DMatrix object
predict,xgb.Booster.handle-method Predict method for eXtreme Gradient Boosting model handle
agaricus.test Test part from Mushroom Data Set
xgb.DMatrix Contruct xgb.DMatrix object
xgb.cv Cross Validation
nrow,xgb.DMatrix-method Number of xgb.DMatrix rows
slice Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object
setinfo Set information of an xgb.DMatrix object
agaricus.train Training part from Mushroom Data Set
xgb.load Load xgboost model from binary file
xgb.DMatrix.save Save xgb.DMatrix object to binary file
xgb.importance Show importance of features in a model
xgb.save Save xgboost model to binary file
xgb.plot.tree Plot a boosted tree model
xgb.dump Save xgboost model to text file
xgb.plot.importance Plot feature importance bar graph
xgb.model.dt.tree Convert tree model dump to data.table
xgb.save.raw Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector
xgb.train eXtreme Gradient Boosting Training
xgboost eXtreme Gradient Boosting (Tree) library
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Last month downloads


Type Package
Date 2016-07-12
License Apache License (== 2.0) | file LICENSE
URL https://github.com/dmlc/xgboost
BugReports https://github.com/dmlc/xgboost/issues
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2016-07-12 07:17:10 UTC; hetong007
Repository CRAN
Date/Publication 2016-07-12 10:55:21

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