AdaBoost-I: AdaBoost_I KEEL Imbalanced Classification Algorithm

Description Usage Arguments Value Examples

Description

AdaBoost_I Imbalanced Classification Algorithm from KEEL.

Usage

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AdaBoost_I(train, test, pruned, confidence, instancesPerLeaf,
   numClassifiers, algorithm, trainMethod, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

pruned

pruned. Default value = TRUE

confidence

confidence. Default value = 0.25

instancesPerLeaf

instancesPerLeaf. Default value = 2

numClassifiers

numClassifiers. Default value = 10

algorithm

algorithm. Default value = "ADABOOST"

trainMethod

trainMethod. Default value = "NORESAMPLING"

seed

Seed for random numbers. If it is not assigned a value, the seed will be a random number

Value

A data.frame with the actual and predicted classes for both train and test datasets.

Examples

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#data_train <- RKEEL::loadKeelDataset("iris_train")
#data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
#algorithm <- RKEEL::AdaBoost_I(data_train, data_test)

#Run algorithm
#algorithm$run()

#See results
#algorithm$testPredictions

RKEEL documentation built on March 19, 2020, 5:09 p.m.

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