NU_SVR-R: NU_SVR_R KEEL Regression Algorithm

Description Usage Arguments Value Examples

Description

NU_SVR_R Regression Algorithm from KEEL.

Usage

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NU_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 = ?

C

C. Default value = ?

eps

eps. Default value = ?

degree

degree. Default value = ?

gamma

gamma. Default value = ?

coef0

coef0. Default value = ?

nu

nu. Default value = ?

p

p. Default value = ?

shrinking

shrinking. Default value = ?

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

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

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

#Run algorithm
#algorithm$run()

#See results
#algorithm$testPredictions

RKEEL documentation built on March 19, 2020, 5:09 p.m.

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