BAGGING: Classification using Bagging

In fdm2id: Data Mining and R Programming for Beginners

Classification using Bagging

Description

Ensemble learning, through Bagging Algorithm.

Usage

BAGGING(
  x,
  y,
  learningmethod,
  nsamples = 100,
  bag.size = nrow(x),
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

x	The dataset (description/predictors), a matrix or data.frame.
y	The target (class labels or numeric values), a factor or vector.
learningmethod	The boosted method.
nsamples	The number of samplings.
bag.size	The size of the samples.
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: BAGGING does not support reusing pre-tuned parameters (the base learner is fitted afresh on each sample).
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: BAGGING does not produce a plot.
seed	A specified seed for random number generation.
...	Other specific parameters for the leaning method.

Value

The classification model.

See Also

ADABOOST, predict.boosting

Examples

require (datasets)
data (iris)
BAGGING (iris [, -5], iris [, 5], NB)

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