xgboost v0.4-2


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


Type Package
Date 2015-08-01
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 2015-08-01 18:21:31 UTC; ubuntu
Repository CRAN
Date/Publication 2015-08-02 08:23:27

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