search for: svmradial

...by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data = trainset, method="svmRadial", tuneGrid = expand.grid(.sigma=c(0.0118),.C=c(8,16,32,64,128)), trControl=MyTrainControl, fit = FALSE ) Once this returns my ideal parameters, in this case Cost of 64, do I simply just re-run the whole process again, passing a grid only containi...

...eger specifying the number of points on the grid for each tuning parameter. data: the training data (only needed in the case where the 'method' is 'cforest', 'earth', 'bagEarth', 'fda', 'bagFDA', 'rpart', 'svmRadial', 'pam', 'lars2', 'rf' or 'pls'). The outcome should be in a column called '.outcome'. and gives the following examples: ------------------------------------------------------------ createGrid("rda", 4) createGrid("lm&...

Hello all, I would like to use the train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2) fitControl$summaryFunction<- Rand svmNew<- train(TrainData, TrainClasses, method = "svmRadial", preProcess = c("center", "scale")...

...nd so on ,how can I do? I have try RFE+SVM, also wrong message:> set.seed(1) > svmProfile<-rfe(trx,try,sizes=c(1:3), + rfeControl=rfeControl(functions=caretFuncs,method="cv", + verbose=F,returnResamp="final",number=10), + method="svmRadial",tuneLength=5) Fitting: sigma=0.009246713, C=0.1 Fitting: sigma=0.009246713, C=1 Fitting: sigma=0.009246713, C=10 Fitting: sigma=0.009246713, C=100 Fitting: sigma=0.009246713, C=1000 Error in rfeControl$functions$rank(fitObject, .x, y) : need importance columns for each class thank yo...

...estindex,-1] ## TUNE caret / kernlab set.seed(1) MyTrainControl=trainControl( method = "repeatedcv", number=10, repeats=5, returnResamp = "all", classProbs = TRUE ) ## MODEL rbfSVM <- train(outcome~., data = trainset, method="svmRadial", preProc = c("scale"), tuneLength = 10, trControl=MyTrainControl, fit = FALSE ) There were 50 or more warnings (use warnings() to see the first 50) > warnings() Warning messages: 1: In train.default(x, y, weights = w, ....

...elow). Can anyone please shed any light on this? Thanks very much! ### To replicate: require(repmis) # For downloading from https df <- source_data('https://dl.dropboxusercontent.com/u/47973221/data.csv', sep=',') require(caret) svm.m1 <- train(df[,-1],df[,1],method='svmRadial',metric='Kappa',tunelength=5,trControl=trainControl(method='repeatedcv', number=10, repeats=10, classProbs=TRUE)) svm.m1 sessionInfo() ### Results - R2.15.2 > svm.m1 1241 samples 7 predictors 10 classes: ?O27479?, ?O31403?, ?O32057?, ?O32059?, ?O32060?, ?O32078?, ?O320...

...list = TRUE) myCtrl <- trainControl(method = "boot", index = tmp, timingSamps = 2, classProbs = TRUE, summaryFunction = twoClassSummary) RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1, metric="ROC") SVMmodel <- train(X,Y,method='svmRadial',trControl=myCtrl,tuneLength=3, metric="ROC") KNNmodel <- train(X,Y,method='knn',trControl=myCtrl,tuneLength=10, metric="ROC") NNmodel <- train(X,Y,method='nnet',trControl=myCtrl,tuneLength=3, trace = FALSE, metric="ROC") ============...

...VM, also wrong message:> > set.seed(1) > > svmProfile<-rfe(trx,try,sizes=c(1:3), > +? ? ? ? ? > ???rfeControl=rfeControl(functions=caretFuncs,method="cv", > +? ? ? ? ? > ???verbose=F,returnResamp="final",number=10), > +? ? ? ? ? > ???method="svmRadial",tuneLength=5) > Fitting: sigma=0.009246713, C=0.1 > Fitting: sigma=0.009246713, C=1 > Fitting: sigma=0.009246713, C=10 > Fitting: sigma=0.009246713, C=100 > Fitting: sigma=0.009246713, C=1000 > Error in rfeControl$functions$rank(fitObject, .x, y) : > ? need importan...