xgboost v1.2.0.1

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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.

Functions in xgboost

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

Name
discoverYourData.Rmd
vignette.css
xgboost.Rnw
xgboost.bib
xgboostPresentation.Rmd
xgboostfromJSON.Rmd
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Details

Type Package
Date 2020-08-28
License Apache License (== 2.0) | file LICENSE
URL https://github.com/dmlc/xgboost
BugReports https://github.com/dmlc/xgboost/issues
NeedsCompilation yes
VignetteBuilder knitr
RoxygenNote 7.1.1
SystemRequirements GNU make, C++14
Packaged 2020-09-02 00:19:39 UTC; ubuntu
Repository CRAN
Date/Publication 2020-09-02 05:40:03 UTC

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