xgboost v0.71.2


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


Type Package
Date 2018-06-08
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 6.0.1
SystemRequirements GNU make, C++11
Packaged 2018-06-08 21:49:47 UTC; ubuntu
Repository CRAN
Date/Publication 2018-06-09 04:24:25 UTC

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