A comparative study of support vector machine and logistic regression article · January 021 citations reads 11 authors

Page 92 
 
Appendix A 
R Programmes Used for Data Analysis in Support Vector Machine 
rm (list = ls()) 
dat = read .table(file='clipboard' ,header=T, sep=",");dat 
colnames (dat) =c ("RAD","AGE","HAA","ARP","DOS","FAD", "class");dat 
head (dat, n=3) 
dat $ class = ifelse (dat $ class==2,-1,1);dat 
n = nrow (dat) 
TrainIndex = sample (1:n, size = round(0.7*n),replace = FALSE) 
Train = dat [TrainIndex,] 
Test = dat [-TrainIndex,] 
Train = as.matrix (Train) 
nrow (Train) + nrow (Test) 
library (kernlab) 
svp = ksvm (Train[, -7],Train[,7],type="C-svc" ,kernel = 'vanilladot',C=100,scaled=c()) 
preds = predict (svp, Test [,-7])
CorrectPrediction = sum (Test [,7] = = preds); CorrectPrediction 
Accuracy = (CorrectPrediction / nrow (Test))*100 
noquote (paste0 ('Accuracy=',Accuracy,'%')) 
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