xgboost v0.90.0.2


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


Type Package
Date 2019-08-01
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 6.1.0
SystemRequirements GNU make, C++11
Packaged 2019-08-01 17:59:16 UTC; htong
Repository CRAN
Date/Publication 2019-08-01 19:20:02 UTC

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