xgboost v1.0.0.2


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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
print.xgb.Booster Print xgb.Booster
dim.xgb.DMatrix Dimensions of xgb.DMatrix
xgb.ggplot.importance Plot feature importance as a bar graph
xgb.DMatrix Construct xgb.DMatrix object
setinfo Set information of an xgb.DMatrix object
xgb.plot.multi.trees Project all trees on one tree and plot it
dimnames.xgb.DMatrix Handling of column names of xgb.DMatrix
xgb.DMatrix.save Save xgb.DMatrix object to binary file
print.xgb.cv.synchronous Print xgb.cv result
cb.save.model Callback closure for saving a model file.
xgb.cv Cross Validation
xgb.dump Dump an xgboost model in text format.
slice Get a new DMatrix containing the specified rows of original xgb.DMatrix object
xgb.load Load xgboost model from binary file
xgb.Booster.complete Restore missing parts of an incomplete xgb.Booster object.
getinfo Get information of an xgb.DMatrix object
xgb.save.raw Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector
xgb.save Save xgboost model to binary file
xgb.model.dt.tree Parse a boosted tree model text dump
xgb.gblinear.history Extract gblinear coefficients history.
xgb.importance Importance of features in a model.
print.xgb.DMatrix Print xgb.DMatrix
xgb.parameters<- Accessors for model parameters.
xgb.plot.shap SHAP contribution dependency plots
xgb.plot.tree Plot a boosted tree model
xgb.ggplot.deepness Plot model trees deepness
xgb.attr Accessors for serializable attributes of a model.
xgb.train eXtreme Gradient Boosting Training
xgb.create.features Create new features from a previously learned model
predict.xgb.Booster Predict method for eXtreme Gradient Boosting model
xgboost-deprecated Deprecation notices.
cb.early.stop Callback closure to activate the early stopping.
agaricus.train Training part from Mushroom Data Set
cb.cv.predict Callback closure for returning cross-validation based predictions.
callbacks Callback closures for booster training.
cb.reset.parameters Callback closure for resetting the booster's parameters at each iteration.
cb.evaluation.log Callback closure for logging the evaluation history
cb.gblinear.history Callback closure for collecting the model coefficients history of a gblinear booster during its training.
agaricus.test Test part from Mushroom Data Set
cb.print.evaluation Callback closure for printing the result of evaluation
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Vignettes of xgboost

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Last month downloads


Type Package
Date 2020-03-25
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 7.0.2
SystemRequirements GNU make, C++11
Packaged 2020-03-25 01:19:19 UTC; ubuntu
Repository CRAN
Date/Publication 2020-03-25 14:10:02 UTC

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