Package: xgboost 1.7.8.1

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://trivialfis.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

12.25 score 6 stars 107 packages 13k scripts 60k downloads 207 mentions 45 exports 4 dependencies

Last updated 4 months agofrom:0e3a1fd399. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 22 2024
R-4.5-linux-x86_64NOTENov 22 2024

Exports:cb.cv.predictcb.early.stopcb.evaluation.logcb.gblinear.historycb.print.evaluationcb.reset.parameterscb.save.modelgetinfosetinfoslicexgb.attrxgb.attr<-xgb.attributesxgb.attributes<-xgb.Booster.completexgb.configxgb.config<-xgb.create.featuresxgb.cvxgb.DMatrixxgb.DMatrix.savexgb.dumpxgb.gblinear.historyxgb.get.configxgb.ggplot.deepnessxgb.ggplot.importancexgb.ggplot.shap.summaryxgb.importancexgb.loadxgb.load.rawxgb.model.dt.treexgb.parameters<-xgb.plot.deepnessxgb.plot.importancexgb.plot.multi.treesxgb.plot.shapxgb.plot.shap.summaryxgb.plot.treexgb.savexgb.save.rawxgb.serializexgb.set.configxgb.trainxgb.unserializexgboost

Dependencies:data.tablejsonlitelatticeMatrix

Understand your dataset with XGBoost

Tianqi Chen, Tong He, Michaël Benesty, Yuan Tang

Rendered fromdiscoverYourData.Rmdusingknitr::rmarkdownon Nov 22 2024.

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

XGBoost from JSON

Roland Stevenson

Rendered fromxgboostfromJSON.Rmdusingknitr::rmarkdownon Nov 22 2024.

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

XGBoost presentation

Tianqi Chen, Tong He, Michaël Benesty

Rendered fromxgboostPresentation.Rmdusingknitr::rmarkdownon Nov 22 2024.

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

xgboost: eXtreme Gradient Boosting

Rendered fromxgboost.Rnwusingknitr::knitron Nov 22 2024.

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

Readme and manuals

Help Manual

Help pageTopics
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 Setagaricus.test
Training part from Mushroom Data Setagaricus.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 historycb.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 evaluationcb.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.DMatrixdim.xgb.DMatrix
Handling of column names of 'xgb.DMatrix'dimnames.xgb.DMatrix dimnames<-.xgb.DMatrix
Get information of an xgb.DMatrix objectgetinfo getinfo.xgb.DMatrix
Scale feature value to have mean 0, standard deviation 1normalize
Predict method for eXtreme Gradient Boosting modelpredict.xgb.Booster predict.xgb.Booster.handle
Combine and melt feature values and SHAP contributions for sample observations.prepare.ggplot.shap.data
Print xgb.Boosterprint.xgb.Booster
Print xgb.cv resultprint.xgb.cv.synchronous
Print xgb.DMatrixprint.xgb.DMatrix
Set information of an xgb.DMatrix objectsetinfo setinfo.xgb.DMatrix
Get a new DMatrix containing the specified rows of original xgb.DMatrix objectslice 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 modelxgb.create.features
Cross Validationxgb.cv
Construct xgb.DMatrix objectxgb.DMatrix
Save xgb.DMatrix object to binary filexgb.DMatrix.save
Dump an xgboost model in text format.xgb.dump
Extract gblinear coefficients history.xgb.gblinear.history
Plot model trees deepnessxgb.ggplot.deepness xgb.plot.deepness
Plot feature importance as a bar graphxgb.ggplot.importance xgb.plot.importance
SHAP contribution dependency summary plotxgb.ggplot.shap.summary xgb.plot.shap.summary
Importance of features in a model.xgb.importance
Load xgboost model from binary filexgb.load
Load serialised xgboost model from R's raw vectorxgb.load.raw
Parse a boosted tree model text dumpxgb.model.dt.tree
Accessors for model parameters.xgb.parameters<-
Project all trees on one tree and plot itxgb.plot.multi.trees
SHAP contribution dependency plotsxgb.plot.shap
Plot a boosted tree modelxgb.plot.tree
Save xgboost model to binary filexgb.save
Save xgboost model to R's raw vector, user can call xgb.load.raw to load the model back from raw vectorxgb.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 configurationxgb.get.config xgb.set.config xgb.set.config, xgb.get.config
eXtreme Gradient Boosting Trainingxgb.train xgboost
Load the instance back from 'xgb.serialize'xgb.unserialize
Deprecation notices.xgboost-deprecated