xgboost v0.6.4.1

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Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. 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
cb.save.model Callback closure for saving a model file.
dim.xgb.DMatrix Dimensions of xgb.DMatrix
agaricus.test Test part from Mushroom Data Set
agaricus.train Training part from Mushroom Data Set
predict.xgb.Booster Predict method for eXtreme Gradient Boosting model
print.xgb.Booster Print xgb.Booster
cb.early.stop Callback closure to activate the early stopping.
cb.evaluation.log Callback closure for logging the evaluation history
dimnames.xgb.DMatrix Handling of column names of xgb.DMatrix
getinfo Get information of an xgb.DMatrix object
xgb.cv Cross Validation
xgb.dump Save xgboost model to text file
xgb.DMatrix Contruct xgb.DMatrix object
xgb.DMatrix.save Save xgb.DMatrix object to binary file
xgb.ggplot.deepness Plot model trees deepness
xgb.ggplot.importance Plot feature importance as a bar graph
xgb.train eXtreme Gradient Boosting Training
xgboost-deprecated Deprecation notices.
cb.print.evaluation Callback closure for printing the result of evaluation
cb.reset.parameters Callback closure for restetting the booster's parameters at each iteration.
print.xgb.DMatrix Print xgb.DMatrix
print.xgb.cv.synchronous Print xgb.cv result
xgb.importance Show importance of features in a model
xgb.load Load xgboost model from binary file
setinfo Set information of an xgb.DMatrix object
slice Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object
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.attr Accessors for serializable attributes of a model.
xgb.create.features Create new features from a previously learned model
callbacks Callback closures for booster training.
cb.cv.predict Callback closure for returning cross-validation based predictions.
xgb.model.dt.tree Parse a boosted tree model text dump
xgb.parameters<- Accessors for model parameters.
xgb.plot.multi.trees Project all trees on one tree and plot it
xgb.plot.tree Plot a boosted tree model
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Vignettes of xgboost

Name
discoverYourData.Rmd
vignette.css
xgboost.Rnw
xgboost.bib
xgboostPresentation.Rmd
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Details

Type Package
Date 2018-01-22
License Apache License (== 2.0) | file LICENSE
URL https://github.com/dmlc/xgboost
BugReports https://github.com/dmlc/xgboost/issues
VignetteBuilder knitr
RoxygenNote 6.0.1
NeedsCompilation yes
Packaged 2018-01-23 19:17:32 UTC; hetong007
Repository CRAN
Date/Publication 2018-01-23 21:57:26 UTC

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