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xgboost (version 0.82.1)

Extreme Gradient Boosting

Description

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) . 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.

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Install

install.packages('xgboost')

Monthly Downloads

54,777

Version

0.82.1

License

Apache License (== 2.0) | file LICENSE

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Maintainer

Tong He

Last Published

March 11th, 2019

Functions in xgboost (0.82.1)

print.xgb.Booster

Print xgb.Booster
cb.save.model

Callback closure for saving a model file.
dimnames.xgb.DMatrix

Handling of column names of xgb.DMatrix
callbacks

Callback closures for booster training.
xgb.DMatrix

Construct xgb.DMatrix object
xgb.load

Load xgboost model from binary file
dim.xgb.DMatrix

Dimensions of xgb.DMatrix
cb.cv.predict

Callback closure for returning cross-validation based predictions.
cb.evaluation.log

Callback closure for logging the evaluation history
xgb.ggplot.importance

Plot feature importance as a bar graph
xgb.DMatrix.save

Save xgb.DMatrix object to binary file
xgb.plot.multi.trees

Project all trees on one tree and plot it
getinfo

Get information of an xgb.DMatrix object
slice

Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object
cb.gblinear.history

Callback closure for collecting the model coefficients history of a gblinear booster during its training.
xgb.Booster.complete

Restore missing parts of an incomplete xgb.Booster object.
predict.xgb.Booster

Predict method for eXtreme Gradient Boosting model
xgb.model.dt.tree

Parse a boosted tree model text dump
xgb.cv

Cross Validation
cb.print.evaluation

Callback closure for printing the result of evaluation
agaricus.test

Test part from Mushroom Data Set
xgb.plot.shap

SHAP contribution dependency plots
agaricus.train

Training part from Mushroom Data Set
xgb.attr

Accessors for serializable attributes of a model.
print.xgb.cv.synchronous

Print xgb.cv result
xgb.create.features

Create new features from a previously learned model
xgb.dump

Dump an xgboost model in text format.
xgb.plot.tree

Plot a boosted tree model
xgb.save

Save xgboost model to binary file
setinfo

Set information of an xgb.DMatrix object
xgb.importance

Importance of features in a model.
xgb.gblinear.history

Extract gblinear coefficients history.
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.parameters<-

Accessors for model parameters.
xgb.ggplot.deepness

Plot model trees deepness
xgboost-deprecated

Deprecation notices.
xgb.train

eXtreme Gradient Boosting Training
cb.reset.parameters

Callback closure for restetting the booster's parameters at each iteration.
cb.early.stop

Callback closure to activate the early stopping.
print.xgb.DMatrix

Print xgb.DMatrix