GFS-GSP-R: GFS_GSP_R KEEL Regression Algorithm

GFS_GSP_RR Documentation

GFS_GSP_R KEEL Regression Algorithm

Description

GFS_GSP_R Regression Algorithm from KEEL.

Usage

GFS_GSP_R(train, test, numLabels, numRules, deltafitsap,
   p0sap, p1sap, amplMut, nsubsap, probOptimLocal,
   numOptimLocal, idOptimLocal, probcrossga, probmutaga,
   lenchaingap, maxtreeheight, numItera, seed)

Arguments

train

Train dataset as a data.frame object

test

Test dataset as a data.frame object

numLabels

numLabels. Default value = 3

numRules

numRules. Default value = 8

deltafitsap

deltafitsap. Default value = 0.5

p0sap

p0sap. Default value = 0.5

p1sap

p1sap. Default value = 0.5

amplMut

amplMut. Default value = 0.1

nsubsap

nsubsap. Default value = 10

probOptimLocal

probOptimLocal. Default value = 0.00

numOptimLocal

numOptimLocal. Default value = 0

idOptimLocal

idOptimLocal. Default value = 0

probcrossga

probcrossga. Default value = 0.5

probmutaga

probmutaga. Default value = 0.5

lenchaingap

lenchaingap. Default value = 10

maxtreeheight

maxtreeheight. Default value = 8

numItera

numItera. Default value = 10000

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 values for both train and test datasets.

Examples

data_train <- RKEEL::loadKeelDataset("autoMPG6_train")
data_test <- RKEEL::loadKeelDataset("autoMPG6_test")

#Create algorithm
algorithm <- RKEEL::GFS_GSP_R(data_train, data_test)
algorithm <- RKEEL::GFS_GSP_R(data_train, data_test, numRules=2, numItera=10, maxtreeheight=2)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

RKEEL documentation built on Sept. 15, 2023, 1:08 a.m.

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