GFS-GSP-R: GFS_GSP_R KEEL Regression Algorithm

In RKEEL: Using 'KEEL' in R Code

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.