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xgboost (version 1.5.0.2)

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

1.5.0.2

License

Apache License (== 2.0) | file LICENSE

Issues

401

Pull Requests

58

Stars

26,297

Forks

8,726

Maintainer

Tong He

Last Published

November 21st, 2021

Functions in xgboost (1.5.0.2)

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
getinfo

Get information of an xgb.DMatrix object
xgb.attr

Accessors for serializable attributes of a model.
xgb.DMatrix.save

Save xgb.DMatrix object to binary file
dimnames.xgb.DMatrix

Handling of column names of xgb.DMatrix
print.xgb.Booster

Print xgb.Booster
prepare.ggplot.shap.data

Combine and melt feature values and SHAP contributions for sample observations.
xgb.Booster.complete

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

Construct xgb.DMatrix object
xgb.cv

Cross Validation
xgb.dump

Dump an xgboost model in text format.
xgb.load

Load xgboost model from binary file
xgb.load.raw

Load serialised xgboost model from R's raw vector
setinfo

Set information of an xgb.DMatrix object
slice

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

Extract gblinear coefficients history.
xgb.plot.shap

SHAP contribution dependency plots
xgb.plot.multi.trees

Project all trees on one tree and plot it
cb.early.stop

Callback closure to activate the early stopping.
predict.xgb.Booster

Predict method for eXtreme Gradient Boosting model
normalize

Scale feature value to have mean 0, standard deviation 1
xgb.train

eXtreme Gradient Boosting Training
xgb.unserialize

Load the instance back from xgb.serialize
xgb.create.features

Create new features from a previously learned model
xgb.config

Accessors for model parameters as JSON string.
xgb.parameters<-

Accessors for model parameters.
xgb.model.dt.tree

Parse a boosted tree model text dump
xgb.ggplot.shap.summary

SHAP contribution dependency summary plot
xgb.importance

Importance of features in a model.
xgb.ggplot.importance

Plot feature importance as a bar graph
xgb.ggplot.deepness

Plot model trees deepness
xgb.plot.tree

Plot a boosted tree model
xgb.set.config, xgb.get.config

Set and get global configuration
xgb.save

Save xgboost model to binary file
xgb.shap.data

Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc. Internal utility function.
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.
xgboost-deprecated

Deprecation notices.
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
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.
cb.print.evaluation

Callback closure for printing the result of evaluation
agaricus.test

Test part from Mushroom Data Set
cb.reset.parameters

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

Training part from Mushroom Data Set
callbacks

Callback closures for booster training.
cb.gblinear.history

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

Callback closure for logging the evaluation history