ADABOOST: Classification using AdaBoost

In fdm2id: Data Mining and R Programming for Beginners

Classification using AdaBoost

Description

Ensemble learning, through AdaBoost Algorithm.

Usage

ADABOOST(
  x,
  y,
  learningmethod,
  nsamples = 100,
  fuzzy = FALSE,
  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.
fuzzy	Indicates whether or not fuzzy classification should be used or not.
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: ADABOOST does not support reusing pre-tuned parameters (the base learner given as learningmethod is tuned independently on each boosting sample).
graph	Present for interface consistency with performance (which always passes it when fitting a model). Currently unused: ADABOOST 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

BAGGING, predict.boosting

Examples

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

fdm2id documentation built on Sept. 4, 2026, 5:07 p.m.