GFS-RB-MF-R: GFS_RB_MF_R KEEL Regression Algorithm

In RKEEL: Using 'KEEL' in R Code

GFS_RB_MF_R KEEL Regression Algorithm

Description

GFS_RB_MF_R Regression Algorithm from KEEL.

Usage

GFS_RB_MF_R(train, test, numLabels, popSize, generations,
   crossProb, mutProb, seed)

Arguments

train	Train dataset as a data.frame object
test	Test dataset as a data.frame object
numLabels	numLabels. Default value = 3
popSize	popSize. Default value = 50
generations	generations. Default value = 100
crossProb	crossProb. Default value = 0.9
mutProb	mutProb. Default value = 0.1
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_RB_MF_R(data_train, data_test)
algorithm <- RKEEL::GFS_RB_MF_R(data_train, data_test, popSize = 5, generations = 10)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

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