xgboost v0.6-4


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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.early.stop Callback closure to activate the early stopping.
cb.save.model Callback closure for saving a model file.
callbacks Callback closures for booster training.
cb.print.evaluation Callback closure for printing the result of evaluation
cb.cv.predict Callback closure for returning cross-validation based predictions.
agaricus.train Training part from Mushroom Data Set
agaricus.test Test part from Mushroom Data Set
dim.xgb.DMatrix Dimensions of xgb.DMatrix
cb.reset.parameters Callback closure for restetting the booster's parameters at each iteration.
cb.evaluation.log Callback closure for logging the evaluation history
dimnames.xgb.DMatrix Handling of column names of xgb.DMatrix
getinfo Get information of an xgb.DMatrix object
print.xgb.cv.synchronous Print xgb.cv result
print.xgb.DMatrix Print xgb.DMatrix
setinfo Set information of an xgb.DMatrix object
xgb.attr Accessors for serializable attributes of a model.
slice Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object
print.xgb.Booster Print xgb.Booster
predict.xgb.Booster Predict method for eXtreme Gradient Boosting model
xgb.create.features Create new features from a previously learned model
xgb.save Save xgboost model to binary file
xgb.ggplot.deepness Plot model trees deepness
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.ggplot.importance Plot feature importance as a bar graph
xgb.load Load xgboost model from binary file
xgb.importance Show importance of features in a model
xgb.plot.tree Plot a boosted tree model
xgb.plot.multi.trees Project all trees on one tree and plot it
xgb.parameters<- Accessors for model parameters.
xgb.model.dt.tree Parse a boosted tree model text dump
xgb.train eXtreme Gradient Boosting Training
xgb.DMatrix.save Save xgb.DMatrix object to binary file
xgb.dump Save xgboost model to text file
xgboost-deprecated Deprecation notices.
xgb.cv Cross Validation
xgb.DMatrix Contruct xgb.DMatrix object
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Type Package
Date 2017-01-04
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 2017-01-05 06:38:47 UTC; hetong007
Repository CRAN
Date/Publication 2017-01-05 10:40:06

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