Package: xgboost 1.7.8.1

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], XGBoost contributors [cph]

xgboost_1.7.8.1.tar.gz
xgboost_1.7.8.1.tar.gz(r-4.5-noble)xgboost_1.7.8.1.tar.gz(r-4.4-noble)
xgboost.pdf |xgboost.html✨
xgboost/json (API)

# Install 'xgboost' in R:

install.packages('xgboost', repos = c('https://cran.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

45 exports 6 stars 7.77 score 4 dependencies 101 dependents 207 mentions 13.1k scripts 53.3k downloads

Last updated 2 months agofrom:0e3a1fd399. Checks:OK: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Aug 24 2024
R-4.5-linux-x86_64	NOTE	Aug 24 2024

Exports:cb.cv.predict cb.early.stop cb.evaluation.log cb.gblinear.history cb.print.evaluation cb.reset.parameters cb.save.model getinfo setinfo slice xgb.attr xgb.attr<-xgb.attributes xgb.attributes<-xgb.Booster.complete xgb.config xgb.config<-xgb.create.features xgb.cv xgb.DMatrix xgb.DMatrix.save xgb.dump xgb.gblinear.history xgb.get.config xgb.ggplot.deepness xgb.ggplot.importance xgb.ggplot.shap.summary xgb.importance xgb.load xgb.load.raw xgb.model.dt.tree xgb.parameters<-xgb.plot.deepness xgb.plot.importance xgb.plot.multi.trees xgb.plot.shap xgb.plot.shap.summary xgb.plot.tree xgb.save xgb.save.raw xgb.serialize xgb.set.config xgb.train xgb.unserialize xgboost

Dependencies:data.table jsonlite lattice Matrix

Understand your dataset with XGBoost

Tianqi Chen, Tong He, Michaël Benesty, Yuan Tang

Rendered fromdiscoverYourData.Rmdusingknitr::rmarkdownon Aug 24 2024.

Last update: 2024-07-25
Started: 2015-03-03

XGBoost from JSON

Roland Stevenson

Rendered fromxgboostfromJSON.Rmdusingknitr::rmarkdownon Aug 24 2024.

Last update: 2023-12-07
Started: 2019-07-25

XGBoost presentation

Tianqi Chen, Tong He, Michaël Benesty

Rendered fromxgboostPresentation.Rmdusingknitr::rmarkdownon Aug 24 2024.

Last update: 2024-07-25
Started: 2015-03-03

xgboost: eXtreme Gradient Boosting

Rendered fromxgboost.Rnwusingknitr::knitron Aug 24 2024.

Last update: 2024-07-25
Started: 2014-09-01

Help page	Topics
Do not use 'saveRDS' or 'save' for long-term archival of models. Instead, use 'xgb.save' or 'xgb.save.raw'.	a-compatibility-note-for-saveRDS-save
Test part from Mushroom Data Set	agaricus.test
Training part from Mushroom Data Set	agaricus.train
Callback closures for booster training.	callbacks
Callback closure for returning cross-validation based predictions.	cb.cv.predict
Callback closure to activate the early stopping.	cb.early.stop
Callback closure for logging the evaluation history	cb.evaluation.log
Callback closure for collecting the model coefficients history of a gblinear booster during its training.	cb.gblinear.history
Callback closure for printing the result of evaluation	cb.print.evaluation
Callback closure for resetting the booster's parameters at each iteration.	cb.reset.parameters
Callback closure for saving a model file.	cb.save.model
Dimensions of xgb.DMatrix	dim.xgb.DMatrix
Handling of column names of 'xgb.DMatrix'	dimnames.xgb.DMatrix dimnames<-.xgb.DMatrix
Get information of an xgb.DMatrix object	getinfo getinfo.xgb.DMatrix
Scale feature value to have mean 0, standard deviation 1	normalize
Predict method for eXtreme Gradient Boosting model	predict.xgb.Booster predict.xgb.Booster.handle
Combine and melt feature values and SHAP contributions for sample observations.	prepare.ggplot.shap.data
Print xgb.Booster	print.xgb.Booster
Print xgb.cv result	print.xgb.cv.synchronous
Print xgb.DMatrix	print.xgb.DMatrix
Set information of an xgb.DMatrix object	setinfo setinfo.xgb.DMatrix
Get a new DMatrix containing the specified rows of original xgb.DMatrix object	slice slice.xgb.DMatrix [.xgb.DMatrix
Accessors for serializable attributes of a model.	xgb.attr xgb.attr<- xgb.attributes xgb.attributes<-
Restore missing parts of an incomplete xgb.Booster object.	xgb.Booster.complete
Accessors for model parameters as JSON string.	xgb.config xgb.config<-
Create new features from a previously learned model	xgb.create.features
Cross Validation	xgb.cv
Construct xgb.DMatrix object	xgb.DMatrix
Save xgb.DMatrix object to binary file	xgb.DMatrix.save
Dump an xgboost model in text format.	xgb.dump
Extract gblinear coefficients history.	xgb.gblinear.history
Plot model trees deepness	xgb.ggplot.deepness xgb.plot.deepness
Plot feature importance as a bar graph	xgb.ggplot.importance xgb.plot.importance
SHAP contribution dependency summary plot	xgb.ggplot.shap.summary xgb.plot.shap.summary
Importance of features in a model.	xgb.importance
Load xgboost model from binary file	xgb.load
Load serialised xgboost model from R's raw vector	xgb.load.raw
Parse a boosted tree model text dump	xgb.model.dt.tree
Accessors for model parameters.	xgb.parameters<-
Project all trees on one tree and plot it	xgb.plot.multi.trees
SHAP contribution dependency plots	xgb.plot.shap
Plot a boosted tree model	xgb.plot.tree
Save xgboost model to binary file	xgb.save
Save xgboost model to R's raw vector, user can call xgb.load.raw to load the model back from raw vector	xgb.save.raw
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.	xgb.serialize
Set and get global configuration	xgb.get.config xgb.set.config xgb.set.config, xgb.get.config
eXtreme Gradient Boosting Training	xgb.train xgboost
Load the instance back from 'xgb.serialize'	xgb.unserialize
Deprecation notices.	xgboost-deprecated

Package: xgboost 1.7.8.1

xgboost: Extreme Gradient Boosting

Understand your dataset with XGBoost

XGBoost from JSON

XGBoost presentation

xgboost: eXtreme Gradient Boosting

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)