xgboost v0.6-2


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by Tong He

Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of gradient boosting framework. 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
dim.xgb.DMatrix Dimensions of xgb.DMatrix
cb.print.evaluation Callback closure for printing the result of evaluation
cb.early.stop Callback closure to activate the early stopping.
cb.evaluation.log Callback closure for logging the evaluation history
callbacks Callback closures for booster training.
agaricus.test Test part from Mushroom Data Set
cb.reset.parameters Callback closure for restetting the booster's parameters at each iteration.
print.xgb.cv.synchronous Print xgb.cv result
print.xgb.DMatrix Print xgb.DMatrix
predict.xgb.Booster Predict method for eXtreme Gradient Boosting model
getinfo Get information of an xgb.DMatrix object
dimnames.xgb.DMatrix Handling of column names of xgb.DMatrix
slice Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object
setinfo Set information of an xgb.DMatrix object
xgb.attr Accessors for serializable attributes of a model.
print.xgb.Booster Print xgb.Booster
xgb.create.features Create new features from a previously learned model
xgb.dump Save xgboost model to text file
xgb.cv Cross Validation
xgb.importance Show importance of features in a model
xgb.DMatrix Contruct xgb.DMatrix object
xgb.DMatrix.save Save xgb.DMatrix object to binary file
xgb.model.dt.tree Parse a boosted tree model text dump
xgb.parameters<- Accessors for model parameters.
xgb.ggplot.importance Plot feature importance as a bar graph
xgb.ggplot.deepness Plot model trees deepness
xgb.load Load xgboost model from binary file
xgb.save Save xgboost model to binary file
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.plot.multi.trees Project all trees on one tree and plot it
xgb.plot.tree Plot a boosted tree model
xgboost-deprecated Deprecation notices.
xgb.train eXtreme Gradient Boosting Training
agaricus.train Training part from Mushroom Data Set
cb.save.model Callback closure for saving a model file.
cb.cv.predict Callback closure for returning cross-validation based predictions.
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Type Package
Date 2016-11-25
License Apache License (== 2.0) | file LICENSE
URL https://github.com/dmlc/xgboost
BugReports https://github.com/dmlc/xgboost/issues
VignetteBuilder knitr
RoxygenNote 5.0.1
NeedsCompilation yes
Packaged 2016-12-17 21:59:49 UTC; hetong007
Repository CRAN
Date/Publication 2016-12-18 11:23:27

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