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xgboost (version 1.2.0.1)

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

60,167

Version

1.2.0.1

License

Apache License (== 2.0) | file LICENSE

Issues

400

Pull Requests

55

Stars

26,314

Forks

8,728

Maintainer

Tong He

Last Published

September 2nd, 2020

Functions in xgboost (1.2.0.1)

cb.evaluation.log

Callback closure for logging the evaluation history
cb.reset.parameters

Callback closure for resetting the booster's parameters at each iteration.
agaricus.test

Test part from Mushroom Data Set
cb.print.evaluation

Callback closure for printing the result of evaluation
cb.gblinear.history

Callback closure for collecting the model coefficients history of a gblinear booster during its training.
cb.cv.predict

Callback closure for returning cross-validation based predictions.
a-compatibility-note-for-saveRDS-save

Do not use saveRDS or save for long-term archival of models. Instead, use xgb.save or xgb.save.raw.
callbacks

Callback closures for booster training.
cb.early.stop

Callback closure to activate the early stopping.
agaricus.train

Training part from Mushroom Data Set
print.xgb.DMatrix

Print xgb.DMatrix
dim.xgb.DMatrix

Dimensions of xgb.DMatrix
cb.save.model

Callback closure for saving a model file.
print.xgb.cv.synchronous

Print xgb.cv result
slice

Get a new DMatrix containing the specified rows of original xgb.DMatrix object
setinfo

Set information of an xgb.DMatrix object
xgb.cv

Cross Validation
predict.xgb.Booster

Predict method for eXtreme Gradient Boosting model
print.xgb.Booster

Print xgb.Booster
xgb.dump

Dump an xgboost model in text format.
xgb.Booster.complete

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

Construct xgb.DMatrix object
xgb.create.features

Create new features from a previously learned model
xgb.plot.shap

SHAP contribution dependency plots
xgb.config

Accessors for model parameters as JSON string.
xgb.plot.tree

Plot a boosted tree model
xgb.plot.multi.trees

Project all trees on one tree and plot it
xgb.ggplot.deepness

Plot model trees deepness
getinfo

Get information of an xgb.DMatrix object
dimnames.xgb.DMatrix

Handling of column names of xgb.DMatrix
xgb.save.raw

Save xgboost model to R's raw vector, user can call xgb.load.raw to load the model back from raw vector
xgb.save

Save xgboost model to binary file
xgboost-deprecated

Deprecation notices.
xgb.parameters<-

Accessors for model parameters.
xgb.attr

Accessors for serializable attributes of a model.
xgb.model.dt.tree

Parse a boosted tree model text dump
xgb.DMatrix.save

Save xgb.DMatrix object to binary file
xgb.train

eXtreme Gradient Boosting Training
xgb.serialize

Serialize the booster instance into R's raw vector. The serialization method differs from xgb.save.raw as the latter one saves only the model but not parameters. This serialization format is not stable across different xgboost versions.
xgb.gblinear.history

Extract gblinear coefficients history.
xgb.ggplot.importance

Plot feature importance as a bar graph
xgb.importance

Importance of features in a model.
xgb.unserialize

Load the instance back from xgb.serialize
xgb.load.raw

Load serialised xgboost model from R's raw vector
xgb.load

Load xgboost model from binary file