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xgboost (version 1.7.7.1)

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

56,736

Version

1.7.7.1

License

Apache License (== 2.0) | file LICENSE

Issues

411

Pull Requests

58

Stars

27,063

Forks

8,784

Maintainer

Jiaming Yuan

Last Published

January 25th, 2024

Functions in xgboost (1.7.7.1)

dim.xgb.DMatrix

Dimensions of xgb.DMatrix
setinfo

Set information of an xgb.DMatrix object
xgb.model.dt.tree

Parse a boosted tree model text dump
xgb.gblinear.history

Extract gblinear coefficients history.
xgb.config

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

Accessors for model parameters.
xgb.dump

Dump an xgboost model in text format.
xgb.DMatrix.save

Save xgb.DMatrix object to binary file
xgb.attr

Accessors for serializable attributes of a model.
slice

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

Create new features from a previously learned model
xgb.cv

Cross Validation
xgb.plot.shap

SHAP contribution dependency plots
xgb.plot.multi.trees

Project all trees on one tree and plot it
xgb.importance

Importance of features in a model.
xgb.DMatrix

Construct xgb.DMatrix object
xgb.ggplot.importance

Plot feature importance as a bar graph
xgb.Booster.complete

Restore missing parts of an incomplete xgb.Booster object.
xgb.ggplot.shap.summary

SHAP contribution dependency summary plot
xgb.ggplot.deepness

Plot model trees deepness
xgb.set.config, xgb.get.config

Set and get global configuration
xgb.plot.tree

Plot a boosted tree model
xgb.train

eXtreme Gradient Boosting Training
xgb.unserialize

Load the instance back from xgb.serialize
xgb.load

Load xgboost model from binary file
xgb.load.raw

Load serialised xgboost model from R's raw vector
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.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.
cb.print.evaluation

Callback closure for printing the result of evaluation
agaricus.train

Training part from Mushroom Data Set
cb.evaluation.log

Callback closure for logging the evaluation history
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.cv.predict

Callback closure for returning cross-validation based predictions.
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.
cb.reset.parameters

Callback closure for resetting the booster's parameters at each iteration.
dimnames.xgb.DMatrix

Handling of column names of xgb.DMatrix
cb.save.model

Callback closure for saving a model file.
print.xgb.Booster

Print xgb.Booster
getinfo

Get information of an xgb.DMatrix object
prepare.ggplot.shap.data

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

Predict method for eXtreme Gradient Boosting model
agaricus.test

Test part from Mushroom Data Set
normalize

Scale feature value to have mean 0, standard deviation 1
print.xgb.cv.synchronous

Print xgb.cv result
print.xgb.DMatrix

Print xgb.DMatrix