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,
  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 paramters instead of a classification model.
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

## Not run: 
require (datasets)
data (iris)
ADABOOST (iris [, -5], iris [, 5], NB)

## End(Not run)

fdm2id documentation built on July 9, 2023, 6:05 p.m.