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xgboost (version 0.4-4)

Extreme Gradient Boosting

Description

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.

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Install

install.packages('xgboost')

Monthly Downloads

60,167

Version

0.4-4

License

Apache License (== 2.0) | file LICENSE

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Last Published

July 12th, 2016

Functions in xgboost (0.4-4)

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