C45_Binarization-C: C45Binarization_C KEEL Classification Algorithm

Description Usage Arguments Value Examples

Description

C45Binarization_C Classification Algorithm from KEEL.

Usage

1
2
C45Binarization_C(train, test, pruned, confidence, instancesPerLeaf,
   binarization, scoreFunction, bts)

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

binarization

binarization. Default value = "OVO"

scoreFunction

scoreFunction. Default value = "WEIGHTED"

bts

bts. Default value = 0.05

Value

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

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
data_train <- RKEEL::loadKeelDataset("iris_train")
data_test <- RKEEL::loadKeelDataset("iris_test")

#Create algorithm
algorithm <- RKEEL::C45Binarization_C(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions


Search within the RKEEL package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.