fdm2id (version 0.9.9)

ADABOOST: Classification using AdaBoost

Description

Ensemble learning, through AdaBoost Algorithm.

Usage

ADABOOST(
  x,
  y,
  learningmethod,
  nsamples = 100,
  fuzzy = FALSE,
  tune = FALSE,
  seed = NULL,
  ...
)

Value

The classification model.

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.

See Also

BAGGING, predict.boosting

Examples

if (FALSE) {
require (datasets)
data (iris)
ADABOOST (iris [, -5], iris [, 5], NB)
}