R-Software SVM prediction example

Student	x	y	Grade
1	4	85	B
2	6	80	B
3	8	92	A
4	4	70	C
5	2	65	C
6	1	60	C
7	5	89	B
8	7	82	B
9	4	81	B
10	6	95	A

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Add library

library(e1071)

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Read the data from csv file

a<-read.csv(file=file.choose(), header=TRUE)

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name the columns

names(a)

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partition the data into testset and trainset

> index <-1:nrow(a)

> testindex <-sample(index,trunc(length(index)*30/100))

> testset <-a[testindex,]

> trainset<-a[-testindex,]

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label them

> x2<-trainset[,2]

> y2<-trainset[,3]

> z2<-trainset[,4]

> x1<-testset[,2]

> y1<-testset[,3]

> z1<-testset[,4]

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Create the dataframe

DF <-data.frame(x=x2,y=y2,z=z2)

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Train the model

 mod <-svm(z~x,data=DF,kernel="linear")

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Predict

prediction <-predict(mod, newdata=data.frame(x=x1))

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Table out against the true values

 tab <-table(pred=prediction,true=z1)

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Result

  true

pred A B C

   A 0 0 0

   B 1 1 0

   C 0 0 1

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Library(kernlab)
Least Square SVM
 mod <-lssvm(z~x,data=DF,kernel="rbfdot")

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Least Square SVM kernal trick


> rbf=rbfdot(15)                                               ; 15 is the value of sigma, kernal scaling parameter
>  mod <-lssvm(z~x,data=DF,kernel=rbf )

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