Learn R Programming
⚠️There's a newer version (1.7.8.1) of this package.Take me there.

xgboost (version 1.4.1.1)

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.

Copy Link

Version

Install

install.packages('xgboost')

Monthly Downloads

60,167

Version

1.4.1.1

License

Apache License (== 2.0) | file LICENSE

Issues

397

Pull Requests

62

Stars

26,270

Forks

8,723

Maintainer

Tong He

Last Published

April 22nd, 2021

Functions in xgboost (1.4.1.1)

dimnames.xgb.DMatrix

Handling of column names of xgb.DMatrix
getinfo

Get information of an xgb.DMatrix object
slice

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

Set information of an xgb.DMatrix object
normalize

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

Accessors for model parameters as JSON string.
xgb.create.features

Create new features from a previously learned model
predict.xgb.Booster

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

Print xgb.DMatrix
print.xgb.cv.synchronous

Print xgb.cv result
xgb.Booster.complete

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

Construct xgb.DMatrix object
xgb.plot.multi.trees

Project all trees on one tree and plot it
xgb.gblinear.history

Extract gblinear coefficients history.
xgb.unserialize

Load the instance back from xgb.serialize
xgb.train

eXtreme Gradient Boosting Training
xgb.load.raw

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

Load xgboost model from binary file
xgb.importance

Importance of features in a model.
xgb.cv

Cross Validation
dim.xgb.DMatrix

Dimensions of xgb.DMatrix
cb.save.model

Callback closure for saving a model file.
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.ggplot.shap.summary

SHAP contribution dependency summary plot
xgb.plot.shap

SHAP contribution dependency plots
xgb.plot.tree

Plot a boosted tree model
xgboost-deprecated

Deprecation notices.
xgb.set.config, xgb.get.config

Set and get global configuration
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.gblinear.history

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

Save xgb.DMatrix object to binary file
prepare.ggplot.shap.data

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

Accessors for serializable attributes of a model.
print.xgb.Booster

Print xgb.Booster
xgb.ggplot.importance

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

Plot model trees deepness
xgb.dump

Dump an xgboost model in text format.
xgb.save

Save xgboost model to binary file
xgb.model.dt.tree

Parse a boosted tree model text dump
xgb.parameters<-

Accessors for model parameters.
xgb.shap.data

Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc. Internal utility function.
cb.print.evaluation

Callback closure for printing the result of evaluation
cb.evaluation.log

Callback closure for logging the evaluation history
agaricus.test

Test part from Mushroom Data Set
callbacks

Callback closures for booster training.
cb.reset.parameters

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

Callback closure to activate the early stopping.
agaricus.train

Training part from Mushroom Data Set