EPSILON_SVR-R: EPSILON_SVR_R KEEL Regression Algorithm

In RKEEL: Using 'KEEL' in R Code

EPSILON_SVR_R KEEL Regression Algorithm

Description

EPSILON_SVR_R Regression Algorithm from KEEL.

Usage

EPSILON_SVR_R(train, test, KernelType, C, eps, degree, gamma,
   coef0, nu, p, shrinking, seed)

Arguments

train	Train dataset as a data.frame object
test	Test dataset as a data.frame object
KernelType	KernelType. Default value = "RBF"
C	C. Default value = 100.0
eps	eps. Default value = 0.001
degree	degree. Default value = 3
gamma	gamma. Default value = 0.01
coef0	coef0. Default value = 0.0
nu	nu. Default value = 0.5
p	p. Default value = 1.0
shrinking	shrinking. Default value = 0
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::EPSILON_SVR_R(data_train, data_test)

#Run algorithm
algorithm$run()

#See results
algorithm$testPredictions

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