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ADABOOST

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

Examples

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

## End(Not run)

fdm2id

Data Mining and R Programming for Beginners

v0.9.5
GPL-3
Authors
Alexandre Blansché [aut, cre]
Initial release

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