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 [[alternative HTML version deleted]]