xgboost v1.3.2.1

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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.cv.predict	Callback closure for returning cross-validation based predictions.
getinfo	Get information of an xgb.DMatrix object
xgb.attr	Accessors for serializable attributes of a model.
xgb.DMatrix.save	Save xgb.DMatrix object to binary file
dimnames.xgb.DMatrix	Handling of column names of xgb.DMatrix
cb.save.model	Callback closure for saving a model file.
cb.early.stop	Callback closure to activate the early stopping.
slice	Get a new DMatrix containing the specified rows of original xgb.DMatrix object
dim.xgb.DMatrix	Dimensions of xgb.DMatrix
setinfo	Set information of an xgb.DMatrix object
cb.evaluation.log	Callback closure for logging the evaluation history
xgb.load	Load xgboost model from binary file
xgb.gblinear.history	Extract gblinear coefficients history.
prepare.ggplot.shap.data	Combine and melt feature values and SHAP contributions for sample observations.
xgb.DMatrix	Construct xgb.DMatrix object
xgb.Booster.complete	Restore missing parts of an incomplete xgb.Booster object.
xgb.ggplot.deepness	Plot model trees deepness
xgboost-deprecated	Deprecation notices.
xgb.ggplot.importance	Plot feature importance as a bar graph
xgb.config	Accessors for model parameters as JSON string.
xgb.create.features	Create new features from a previously learned model
xgb.model.dt.tree	Parse a boosted tree model text dump
print.xgb.Booster	Print xgb.Booster
xgb.save	Save xgboost model to binary file
xgb.save.raw	Save xgboost model to R's raw vector, user can call xgb.load.raw to load the model back from raw vector
xgb.load.raw	Load serialised xgboost model from R's raw vector
normalize	Scale feature value to have mean 0, standard deviation 1
predict.xgb.Booster	Predict method for eXtreme Gradient Boosting model
cb.gblinear.history	Callback closure for collecting the model coefficients history of a gblinear booster during its training.
xgb.importance	Importance of features in a model.
xgb.train	eXtreme Gradient Boosting Training
xgb.parameters<-	Accessors for model parameters.
xgb.cv	Cross Validation
xgb.unserialize	Load the instance back from xgb.serialize
xgb.ggplot.shap.summary	SHAP contribution dependency summary plot
xgb.plot.multi.trees	Project all trees on one tree and plot it
xgb.plot.shap	SHAP contribution dependency plots
xgb.plot.tree	Plot a boosted tree model
xgb.dump	Dump an xgboost model in text format.
xgb.shap.data	Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc. Internal utility function.
xgb.serialize	Serialize the booster instance into R's raw vector. The serialization method differs from xgb.save.raw as the latter one saves only the model but not parameters. This serialization format is not stable across different xgboost versions.
agaricus.test	Test part from Mushroom Data Set
callbacks	Callback closures for booster training.
cb.print.evaluation	Callback closure for printing the result of evaluation
cb.reset.parameters	Callback closure for resetting the booster's parameters at each iteration.
a-compatibility-note-for-saveRDS-save	Do not use saveRDS or save for long-term archival of models. Instead, use xgb.save or xgb.save.raw.
agaricus.train	Training part from Mushroom Data Set
print.xgb.DMatrix	Print xgb.DMatrix
print.xgb.cv.synchronous	Print xgb.cv result
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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	2021-01-14
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	7.1.1
SystemRequirements	GNU make, C++14
Packaged	2021-01-15 00:22:10 UTC; ubuntu
Repository	CRAN
Date/Publication	2021-01-18 10:10:03 UTC

imports	, data.table (>= 1.9.6) , magrittr (>= 1.5) , Matrix (>= 1.1-0) , methods
suggests	Ckmeans.1d.dp (>= 3.3.1) , crayon , DiagrammeR (>= 0.9.0) , float , ggplot2 (>= 1.0.1) , igraph (>= 1.0.1) , jsonlite , knitr , lintr , rmarkdown , testthat , titanic , vcd (>= 1.3)
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