GRADIENTBOOSTING: Classification using Gradient Boosting

In fdm2id: Data Mining and R Programming for Beginners

Classification using Gradient Boosting

Description

This function builds a classification model using Gradient Boosting

Usage

GRADIENTBOOSTING(
  train,
  labels,
  ntree = 500,
  learningrate = 0.3,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

train	The training set (description), as a data.frame.
labels	Class labels of the training set (vector or factor).
ntree	The number of trees in the forest.
learningrate	The learning rate (between 0 and 1).
tune	If true, the function returns parameters instead of a classification model.
methodparameters	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: GRADIENTBOOSTING does not yet implement hyperparameter tuning.
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: GRADIENTBOOSTING does not produce a plot.
seed	A specified seed for random number generation (row/column subsampling, if used via ...; xgboost's default parameters are otherwise deterministic, but the seed is provided for consistency with the rest of the package's API and to cover subsampling parameters passed through ...).
...	Other parameters.

Value

The classification model.

See Also

xgboost

Examples

require (datasets)
data (iris)
GRADIENTBOOSTING (iris [, -5], iris [, 5])

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.