Extreme Gradient Boosting

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Documentation for package ‘xgboost’ version 0.6-0

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