search for: tunecontrol

Hi I am trying to fit an svm to predict speech recognition errors. I am using best.svm like this: svm.model = best.svm(data[1:3000,1:23],data[1:3000,24],tunecontrol = tune.control()) I got this: > print(svm.model) Call: best.svm(x = data[1:3000, 1:23], tunecontrol = tune.control(), data[1:3000, 24]) Parameters: SVM-Type: eps-regression SVM-Kernel: radial cost: 1 gamma: 0.04347826 epsilon: 0.1 Number of Support Ve...

...od? Let me be more clear with an example data(iris) ## tune `svm' for classification with RBF-kernel (default in svm), ## using one split for training/validation set obj <- tune(svm, Species~., data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)), tunecontrol = tune.control(sampling = "fix") ) ## alternatively: ## obj <- tune.svm(Species~., data = iris, gamma = 2^(-1:1), cost = 2^(2:4)) summary(obj) plot(obj) --------------------------------- For tune, an object of class tune, including the components: best.parameter...

...giving an error saying missing values are not allowed.... again here is the script for BreastCancer Data, library(e1071) library(mda) trdata<-data.frame(train,row.names=NULL) attach(trdata) xtr <- subset(trdata, select = -Class) ytr <- Class bestpara <-tune.knn(xtr,ytr, k = 1:25, tunecontrol = tune.control(sampling = "cross")) and here i got the mentioned error. can anybody help me in this regard... Thanks & Regards, Uttam Phulwale Tata Consultancy Services Limited Mailto: uttam.phulwale@tcs.com Website: http://www.tcs.com [[alternative HTML version deleted]]

...training <- 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,...

...; attach(dd); tr_in = as.matrix(dd[,2:(1 + 1)]); tr_out = dd[,1]; val_in = as.matrix(ddv[,2:(1 + 1)]); val_out = ddv[,1]; t = tune(svm, kernel = "radial", train.x = tr_in, train.y = tr_out, validation.x = val_in, validation.y = val_out, range = list( gamma = 2^(-1:1), cost = 2^(2:4) ), tunecontrol = tune.control(sampling = "fix") ) z = t$best.model save(z, file = "./models/1/20", compress=FALSE); Thanks Pau.

...a <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol = 10), matrix(rnorm(1500, mean = 5, sd = 5),ncol = 10))) class <- as.factor(rep(1:2, each=150)) data<- cbind(data,class) tuned<-best.svm(class~., data=data, kernel = "linear", cost = seq(0.24,0.44, by = .01), tunecontrol=tune.control(cross=300) ) # test with train data predicts <- predict(model, data, probability=TRUE, decision.values = TRUE) tab<-table(predicts, data$class) tab/ This is what I face: /Parameters: SVM-Type: C-classification SVM-Kernel: linear cost: 0.26 gamma: 0.1 N...

...nto .csv file and loaded it into R. > 2) then I invoked e-1071 package and used the following code to tune the svm. >> retune<-tune(svm, Rexp~.,data=retrain, ranges=list(gamma=c(2^-10,2^-8,2^-6,2^-4, 2^-2, > 2^0, 2^1, 2^2, 2^3), cost=c(0.1,0.4,0.6,0.8,1.0,1.2,1.4), > eps=10^(2:1)),tunecontrol=tune.control(sampling="cross")). > 3) I got the following error message. > Error in predict.svm(ret, xhold, decision.values = TRUE) : > ?Model is empty! > 4) 10 random data as per your instructions are: > structure(list(Peb = c(225.0282, 514.4835, 66.6346, 221.4287, > 36...

Hi all, I am using the "svm" command in the e1071 package. Does it have an automatic way of setting the "cost" parameter? I changed a few values for the "cost" parameter but I hope there is a systematic way of obtaining the best "cost" value. I noticed that there is a "cross" (Cross validation) parameter in the "svm" function. But I