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xgboost (version 0.6-2)

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

54,777

Version

0.6-2

License

Apache License (== 2.0) | file LICENSE

Issues

414

Pull Requests

61

Stars

27,058

Forks

8,784

Maintainer

Tong He

Last Published

December 18th, 2016

Functions in xgboost (0.6-2)

dim.xgb.DMatrix

Dimensions of xgb.DMatrix
cb.print.evaluation

Callback closure for printing the result of evaluation
cb.early.stop

Callback closure to activate the early stopping.
cb.evaluation.log

Callback closure for logging the evaluation history
callbacks

Callback closures for booster training.
agaricus.test

Test part from Mushroom Data Set
cb.reset.parameters

Callback closure for restetting the booster's parameters at each iteration.
print.xgb.cv.synchronous

Print xgb.cv result
print.xgb.DMatrix

Print xgb.DMatrix
predict.xgb.Booster

Predict method for eXtreme Gradient Boosting model
getinfo

Get information of an xgb.DMatrix object
dimnames.xgb.DMatrix

Handling of column names of xgb.DMatrix
slice

Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object
setinfo

Set information of an xgb.DMatrix object
xgb.attr

Accessors for serializable attributes of a model.
print.xgb.Booster

Print xgb.Booster
xgb.create.features

Create new features from a previously learned model
xgb.dump

Save xgboost model to text file
xgb.cv

Cross Validation
xgb.importance

Show importance of features in a model
xgb.DMatrix

Contruct xgb.DMatrix object
xgb.DMatrix.save

Save xgb.DMatrix object to binary file
xgb.model.dt.tree

Parse a boosted tree model text dump
xgb.parameters<-

Accessors for model parameters.
xgb.ggplot.importance

Plot feature importance as a bar graph
xgb.ggplot.deepness

Plot model trees deepness
xgb.load

Load xgboost model from binary file
xgb.save

Save xgboost model to binary file
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.plot.multi.trees

Project all trees on one tree and plot it
xgb.plot.tree

Plot a boosted tree model
xgboost-deprecated

Deprecation notices.
xgb.train

eXtreme Gradient Boosting Training
agaricus.train

Training part from Mushroom Data Set
cb.save.model

Callback closure for saving a model file.
cb.cv.predict

Callback closure for returning cross-validation based predictions.