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.">

xgboost: Extreme Gradient Boosting (original) (raw)

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.

Version: 1.7.10.1
Depends: R (≥ 3.3.0)
Imports: Matrix (≥ 1.1-0), methods, data.table (≥ 1.9.6), jsonlite (≥ 1.0)
Suggests: knitr, rmarkdown, ggplot2 (≥ 1.0.1), DiagrammeR (≥ 0.9.0), Ckmeans.1d.dp (≥ 3.3.1), vcd (≥ 1.3), cplm, e1071, caret, testthat, lintr, igraph (≥ 1.0.1), float, crayon, titanic
Published: 2025-04-22
DOI: 10.32614/CRAN.package.xgboost
Author: Tianqi Chen [aut], Tong He [aut], Michael Benesty [aut], Vadim Khotilovich [aut], Yuan Tang ORCID iD [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] (base XGBoost implementation)
Maintainer: Jiaming Yuan <jm.yuan at outlook.com>
BugReports: https://github.com/dmlc/xgboost/issues
License: Apache License (== 2.0) | file
URL: https://github.com/dmlc/xgboost
NeedsCompilation: yes
SystemRequirements: GNU make, C++17
In views: HighPerformanceComputing, MachineLearning, ModelDeployment, Survival
CRAN checks: xgboost results [issues need fixing before 2025-05-09]

Documentation:

Downloads:

Reverse dependencies:

Reverse depends: LogisticEnsembles, NumericEnsembles, PIE
Reverse imports: adapt4pv, alookr, audrex, autoBagging, autostats, bambu, BayesSpace, BioPred, CausalGPS, causalweight, ccmap, cpfa, CRE, creditmodel, csmpv, CytoProfile, dblr, ddml, DeepLearningCausal, DICEM, DSAM, DSWE, EFAfactors, EHRmuse, EIX, FastRet, fastrmodels, GeneralisedCovarianceMeasure, glmnetr, GNET2, GPCERF, iimi, imanr, ImHD, infinityFlow, inTrees, irboost, IVDML, latentFactoR, ldmppr, LTFHPlus, MAPFX, MBMethPred, mikropml, mixgb, modeltime, MSclassifR, nfl4th, nflfastR, nsga3, oncrawlR, personalized, postcard, PoweREST, predhy, predhy.GUI, predictoR, PriceIndices, promor, radiant.model, reddPrec, ReSurv, RIIM, rminer, roseRF, sae.projection, scDblFinder, scds, SELF, SEMdeep, sentiment.ai, SHAPforxgboost, shapviz, simPop, surveyvoi, tidybins, traineR, TSCI, tsensembler, twang, visaOTR, wactor, weightedGCM, xgb2sql, xpect, xrf
Reverse suggests: BAGofT, bigsnpr, biomod2, Boruta, breakDown, bundle, butcher, ClassifyR, coefplot, comets, cornet, cuda.ml, CytoMethIC, DALEXtra, drape, easyalluvial, embed, explore, familiar, fastml, fdm2id, FLAME, flevr, flowml, forecastML, GenericML, lime, LLMAgentR, MachineShop, MantaID, marginaleffects, mcboost, MIC, miesmuschel, mistyR, mlflow, mllrnrs, mlr, mlr3benchmark, mlr3hyperband, mlr3learners, mlr3shiny, mlr3tuning, mlr3tuningspaces, mlr3viz, mlsurvlrnrs, modelStudio, modeltime.ensemble, nlpred, offsetreg, ParBayesianOptimization, parsnip, pathMED, PatientLevelPrediction, pdp, PheCAP, pmml, polle, qeML, r2pmml, rattle, rBayesianOptimization, sense, shapr, sits, stackgbm, SuperLearner, superMICE, superml, survex, targeted, tidypredict, tidysdm, treeshap, tune, twangMediation, utiml, vetiver, vimp, vivid, XAItest
Reverse enhances: fastshap, vip

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