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: 1.7.11.1 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: 2025-05-15 DOI: 10.32614/CRAN.package.xgboost Author: 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] (base XGBoost implementation) Maintainer: Jiaming Yuan <jm.yuan at outlook.com> BugReports: https://github.com/dmlc/xgboost/issues License: Apache License (== 2.0) | file LICENSE URL: https://github.com/dmlc/xgboost NeedsCompilation: yes SystemRequirements: GNU make, C++17 In views: HighPerformanceComputing, MachineLearning, ModelDeployment, Survival CRAN checks: xgboost results 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, LTFGRS, LTFHPlus, MAPFX, MBMethPred, mikropml, mixgb, modeltime, MSclassifR, nfl4th, nflfastR, nsga3, oncrawlR, personalized, PND.heter.cluster, 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, vetiver, vimp, vivid, XAItest Reverse enhances: fastshap, vip Linking:Please use the canonical form https://CRAN.R-project.org/package=xgboost to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4