Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
# Lab 3 block 1 of 732A99/TDDE01/732A68 Machine Learning
# Author: jose.m.pena@liu.se
# Made for teaching purposes
library(kernlab)
set.seed(1234567890)
data(spam)
foo <- sample(nrow(spam))
spam <- spam[foo,]
tr <- spam[1:3000, ]
va <- spam[3001:3800, ]
trva <- spam[1:3800, ]
te <- spam[3801:4601, ]
by <- 0.3
err_va <- NULL
for(i in seq(by,5,by)){
filter <- ksvm(type~.,data=tr,kernel="rbfdot",kpar=list(sigma=0.05),C=i,scaled=FALSE)
mailtype <- predict(filter,va[,-58])
t <- table(mailtype,va[,58])
err_va <-c(err_va,(t[1,2]+t[2,1])/sum(t))
}
filter0 <- ksvm(type~.,data=tr,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
mailtype <- predict(filter0,va[,-58])
t <- table(mailtype,va[,58])
err0 <- (t[1,2]+t[2,1])/sum(t)
err0
filter1 <- ksvm(type~.,data=tr,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
mailtype <- predict(filter1,te[,-58])
t <- table(mailtype,te[,58])
err1 <- (t[1,2]+t[2,1])/sum(t)
err1
filter2 <- ksvm(type~.,data=trva,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
mailtype <- predict(filter2,te[,-58])
t <- table(mailtype,te[,58])
err2 <- (t[1,2]+t[2,1])/sum(t)
err2
filter3 <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
mailtype <- predict(filter3,te[,-58])
t <- table(mailtype,te[,58])
err3 <- (t[1,2]+t[2,1])/sum(t)
err3
# Questions
# 1. Which filter do we return to the user ? filter0, filter1, filter2 or filter3? Why?
# 2. What is the estimate of the generalization error of the filter returned to the user? err0, err1, err2 or err3? Why?
# 3. Implementation of SVM predictions.
gaussian_kernel <- function(x_i, x_star, sigma) {
return(exp(-sum((x_i - x_star)^2) / (2 * sigma^2)))
}
sv<-alphaindex(filter3)[[1]]
co<-coef(filter3)[[1]]
inte<- - b(filter3)
k<-NULL
for(i in 1:10){ # We produce predictions for just the first 10 points in the dataset.
k2<-0
test_point <- spam[i, -58]
support_vector <- spam[sv[j], -58]
kernel_value <- gaussian_kernel(support_vector, test_point, sigma = 0.05)
k2 <- k2 + co[j] * kernel_value
k2 <- k2 + inte
k <- c(k, sign(k2))
predict(filter3,spam[1:10,-58], type = "decision")