Package: xgboost 3.0.0.1

Jiaming Yuan

xgboost: 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.

Authors:Tianqi Chen [aut], Tong He [aut], Michael Benesty [aut], Vadim Khotilovich [aut], Yuan Tang [aut], Hyunsu Cho [aut], Kailong Chen [aut], Rory Mitchell [aut], Ignacio Cano [aut], Tianyi Zhou [aut], Mu Li [aut], Junyuan Xie [aut], Min Lin [aut], Yifeng Geng [aut], Yutian Li [aut], Jiaming Yuan [aut, cre], David Cortes [aut], XGBoost contributors [cph]

xgboost_3.0.0.1.tar.gz
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xgboost.pdf |xgboost.html✨
xgboost/json (API)

# Install 'xgboost' in R:

install.packages('xgboost', repos = c('https://dmlc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dmlc/xgboost/issues

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

agaricus.test - Test part from Mushroom Data Set
agaricus.train - Training part from Mushroom Data Set

On CRAN:

distributed-systems gbdt gbm gbrt machine-learning xgboost cpp openmp

17.45 score 27k stars 115 packages 59k downloads 207 mentions 57 exports 4 dependencies

Last updated 3 hours agofrom:c12b04964d (on release_3.0.0). Checks:1 OK, 8 NOTE, 3 WARNING. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 27 2025
R-4.5-win-x86_64	WARNING	Mar 27 2025
R-4.5-mac-x86_64	NOTE	Mar 27 2025
R-4.5-mac-aarch64	NOTE	Mar 27 2025
R-4.5-linux-x86_64	NOTE	Mar 27 2025
R-4.4-win-x86_64	WARNING	Mar 27 2025
R-4.4-mac-x86_64	NOTE	Mar 27 2025
R-4.4-mac-aarch64	NOTE	Mar 27 2025
R-4.4-linux-x86_64	NOTE	Mar 27 2025
R-4.3-win-x86_64	WARNING	Mar 27 2025
R-4.3-mac-x86_64	NOTE	Mar 27 2025
R-4.3-mac-aarch64	NOTE	Mar 27 2025

Exports:getinfo setinfo xgb.attr xgb.attr<-xgb.attributes xgb.attributes<-xgb.Callback xgb.cb.cv.predict xgb.cb.early.stop xgb.cb.evaluation.log xgb.cb.gblinear.history xgb.cb.print.evaluation xgb.cb.reset.parameters xgb.cb.save.model xgb.config xgb.config<-xgb.copy.Booster xgb.create.features xgb.cv xgb.DataBatch xgb.DataIter xgb.DMatrix xgb.DMatrix.hasinfo xgb.DMatrix.save xgb.dump xgb.ExtMemDMatrix xgb.gblinear.history xgb.get.config xgb.get.DMatrix.data xgb.get.DMatrix.num.non.missing xgb.get.DMatrix.qcut xgb.get.num.boosted.rounds xgb.ggplot.deepness xgb.ggplot.importance xgb.ggplot.shap.summary xgb.importance xgb.is.same.Booster xgb.load xgb.load.raw xgb.model.dt.tree xgb.model.parameters<-xgb.params xgb.plot.deepness xgb.plot.importance xgb.plot.multi.trees xgb.plot.shap xgb.plot.shap.summary xgb.plot.tree xgb.QuantileDMatrix xgb.QuantileDMatrix.from_iterator xgb.save xgb.save.raw xgb.set.config xgb.slice.Booster xgb.slice.DMatrix xgb.train xgboost

Dependencies:data.table jsonlite lattice Matrix

XGBoost for R introduction

Rendered fromxgboost_introduction.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2025-02-19
Started: 2025-01-15

XGBoost from JSON

Roland Stevenson

Rendered fromxgboostfromJSON.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2025-02-19
Started: 2019-05-15

Help page	Topics
Model Serialization and Compatibility	a-compatibility-note-for-saveRDS-save
Test part from Mushroom Data Set	agaricus.test
Training part from Mushroom Data Set	agaricus.train
Extract coefficients from linear booster	coef.xgb.Booster
Dimensions of xgb.DMatrix	dim.xgb.DMatrix
Handling of column names of 'xgb.DMatrix'	dimnames.xgb.DMatrix dimnames<-.xgb.DMatrix
Get or set information of xgb.DMatrix and xgb.Booster objects	getinfo getinfo.xgb.Booster getinfo.xgb.DMatrix setinfo setinfo.xgb.Booster setinfo.xgb.DMatrix
Predict method for XGBoost model	predict.xgb.Booster
Compute predictions from XGBoost model on new data	predict.xgboost
Print xgb.Booster	print.xgb.Booster
Print xgb.cv result	print.xgb.cv.synchronous
Print xgb.DMatrix	print.xgb.DMatrix
Print info from XGBoost model	print.xgboost
Get Features Names from Booster	variable.names.xgb.Booster
Accessors for serializable attributes of a model	xgb.attr xgb.attr<- xgb.attributes xgb.attributes<-
XGBoost Callback Constructor	xgb.Callback
Callback for returning cross-validation based predictions	xgb.cb.cv.predict
Callback to activate early stopping	xgb.cb.early.stop
Callback for logging the evaluation history	xgb.cb.evaluation.log
Callback for collecting coefficients history of a gblinear booster	xgb.cb.gblinear.history
Callback for printing the result of evaluation	xgb.cb.print.evaluation
Callback for resetting booster parameters at each iteration	xgb.cb.reset.parameters
Callback for saving a model file	xgb.cb.save.model
Accessors for model parameters as JSON string	xgb.config xgb.config<-
Deep-copies a Booster Object	xgb.copy.Booster
Create new features from a previously learned model	xgb.create.features
Cross Validation	xgb.cv
Structure for Data Batches	xgb.DataBatch
XGBoost Data Iterator	xgb.DataIter
Construct xgb.DMatrix object	xgb.DMatrix xgb.QuantileDMatrix
Check whether DMatrix object has a field	xgb.DMatrix.hasinfo
Save xgb.DMatrix object to binary file	xgb.DMatrix.save
Dump an XGBoost model in text format.	xgb.dump
DMatrix from External Data	xgb.ExtMemDMatrix
Extract gblinear coefficients history	xgb.gblinear.history
Get DMatrix Data	xgb.get.DMatrix.data
Get Number of Non-Missing Entries in DMatrix	xgb.get.DMatrix.num.non.missing
Get Quantile Cuts from DMatrix	xgb.get.DMatrix.qcut
Get number of boosting in a fitted booster	length.xgb.Booster xgb.get.num.boosted.rounds
Plot model tree depth	xgb.ggplot.deepness xgb.plot.deepness
Plot feature importance	xgb.ggplot.importance xgb.plot.importance
SHAP summary plot	xgb.ggplot.shap.summary xgb.plot.shap.summary
Feature importance	xgb.importance
Check if two boosters share the same C object	xgb.is.same.Booster
Load XGBoost model from binary file	xgb.load
Load serialised XGBoost model from R's raw vector	xgb.load.raw
Parse model text dump	xgb.model.dt.tree
Accessors for model parameters	xgb.model.parameters<-
XGBoost Parameters	xgb.params
Project all trees on one tree	xgb.plot.multi.trees
SHAP dependence plots	xgb.plot.shap
Plot boosted trees	xgb.plot.tree
QuantileDMatrix from External Data	xgb.QuantileDMatrix.from_iterator
Save XGBoost model to binary file	xgb.save
Save XGBoost model to R's raw vector	xgb.save.raw
Set and get global configuration	xgb.get.config xgb.set.config xgb.set.config, xgb.get.config
Slice Booster by Rounds	xgb.slice.Booster [.xgb.Booster
Slice DMatrix	xgb.slice.DMatrix [.xgb.DMatrix
Fit XGBoost Model	xgb.train
Fit XGBoost Model	xgboost
XGBoost Options	xgboost-options

Package: xgboost 3.0.0.1

xgboost: Extreme Gradient Boosting

XGBoost for R introduction

XGBoost from JSON

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)