xgboost v0.90.0.2

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by Tong He View Source

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.print.evaluation	Callback closure for printing the result of evaluation
agaricus.test	Test part from Mushroom Data Set
cb.early.stop	Callback closure to activate the early stopping.
xgb.ggplot.importance	Plot feature importance as a bar graph
callbacks	Callback closures for booster training.
slice	Get a new DMatrix containing the specified rows of original xgb.DMatrix object
agaricus.train	Training part from Mushroom Data Set
cb.cv.predict	Callback closure for returning cross-validation based predictions.
print.xgb.Booster	Print xgb.Booster
cb.evaluation.log	Callback closure for logging the evaluation history
xgboost-deprecated	Deprecation notices.
xgb.create.features	Create new features from a previously learned model
xgb.attr	Accessors for serializable attributes of a model.
xgb.train	eXtreme Gradient Boosting Training
cb.gblinear.history	Callback closure for collecting the model coefficients history of a gblinear booster during its training.
print.xgb.cv.synchronous	Print xgb.cv result
xgb.gblinear.history	Extract gblinear coefficients history.
dim.xgb.DMatrix	Dimensions of xgb.DMatrix
dimnames.xgb.DMatrix	Handling of column names of xgb.DMatrix
print.xgb.DMatrix	Print xgb.DMatrix
cb.save.model	Callback closure for saving a model file.
xgb.parameters<-	Accessors for model parameters.
xgb.ggplot.deepness	Plot model trees deepness
xgb.model.dt.tree	Parse a boosted tree model text dump
xgb.load	Load xgboost model from binary file
xgb.plot.multi.trees	Project all trees on one tree and plot it
predict.xgb.Booster	Predict method for eXtreme Gradient Boosting model
getinfo	Get information of an xgb.DMatrix object
cb.reset.parameters	Callback closure for resetting the booster's parameters at each iteration.
xgb.Booster.complete	Restore missing parts of an incomplete xgb.Booster object.
xgb.plot.tree	Plot a boosted tree model
xgb.cv	Cross Validation
xgb.importance	Importance of features in a model.
xgb.dump	Dump an xgboost model in text format.
xgb.save	Save xgboost model to binary file
xgb.plot.shap	SHAP contribution dependency plots
xgb.DMatrix.save	Save xgb.DMatrix object to binary file
xgb.DMatrix	Construct xgb.DMatrix object
setinfo	Set information of an xgb.DMatrix object
xgb.save.raw	Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector
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Vignettes of xgboost

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

Type	Package
Date	2019-08-01
License	Apache License (== 2.0) \| file LICENSE
URL	https://github.com/dmlc/xgboost
BugReports	https://github.com/dmlc/xgboost/issues
NeedsCompilation	yes
VignetteBuilder	knitr
RoxygenNote	6.1.0
SystemRequirements	GNU make, C++11
Packaged	2019-08-01 17:59:16 UTC; htong
Repository	CRAN
Date/Publication	2019-08-01 19:20:02 UTC

suggests	Ckmeans.1d.dp (>= 3.3.1) , DiagrammeR (>= 0.9.0) , float , ggplot2 (>= 1.0.1) , igraph (>= 1.0.1) , jsonlite , knitr , lintr , rmarkdown , testthat , vcd (>= 1.3)
imports	data.table (>= 1.9.6) , magrittr (>= 1.5) , Matrix (>= 1.1-0) , methods , stringi (>= 0.5.2)
depends	R (>= 3.3.0)
Contributors	Tianqi Chen, Yuan Tang, Michael Benesty, Vadim Khotilovich, Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou, Mu Li, Junyuan Xie, Min Lin, Yifeng Geng, Yutian Li, XGBoost contributors