Hello
I am playing around trying to bootstrap an svm model using a training set and a
test set. I've written another function, auc, which I call here, and am
bootstrapping. I did this successfully with logistic regression, but I am
getting an error from the starred ** line which I determined with print
statements. How do I tune an svm in a bootstrap? I can't find sample code
anywhere.
Code:
library(e1071)
library(boot)
source("hw2a.r")
D <- read.csv("colonoscopy.csv", header=T)
E <- read.csv("CLStest.csv", header=T)
dataclstraining <- subset(D,select=c(....))
classesclstraining <- subset(D, select=Class)
dataclstest <- subset(E,select=c(.......))
classesclstest <- subset(E, select=Class)
bootsvm <- function(data, new_data, newdata_classes, indices)
{
d <-data[indices,]
** model2 <- best.svm(Class~.,data=d, gamma = 10^(-6:-1), cost = 10^(-1:1),
tunecontrol=tune.control(sampling="bootstrap", nboot=1000, boot.size=8/8))
pred.b <- predict(model2, newdata=new_data, decision.values=FALSE,
probability=FALSE)
return(auc(pred.b, t(newdata_classes)))
}
colon.boot <- boot(data=dataclstraining, statistic=bootsvm, R=1000,
new_data=dataclstest, newdata_classes=classesclstest)
ci <- boot.ci(colon.boot)
print(summary(colon.boot$t))
print(ci)
Can anyone point out what I am doing wrong? I am getting a whole host of
errors no matter what I use for the best.svm line. Some variants I have tried
include trying to use the validation.x, validation.y parameters for tune, but I
have no clue how to use them or what they are for. I have also tried the
following line:
model <- best.svm(Class~.,data=dataclstraining, gamma = 10^(-7:-2), cost =
10^(-2:1), tunecontrol=tune.control(cross=8, sampling="cross"))
to no avail. I get NaN/Inf (arg 4) errors. I am so stuck. Please help.
Thank you.
Vaibhav
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