search for: tunelength

...== library(caret) tmp <- createDataPartition(Y, p = 9/10, times = 3, 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',trCon...

...for help. Would someone be willing to help me? Thanks, Robin http://www.epa.gov/wed/pages/models/rivpacs/rivpacs.htm > TrainDataDFAgrps2 <-predcal > TrainClassesDFAgrps2 <-grp.2; > DFAgrps2Fit1 <- train(TrainDataDFAgrps2, TrainClassesDFAgrps2, + method = "lda", + tuneLength = 10, + trControl = trainControl(method = "cv")); Error in train.default(TrainDataDFAgrps2, TrainClassesDFAgrps2, method = "lda", : wrong model type for regression > RFgrps2Fit1 <- train(TrainDataRFgrps2, TrainClassesRFgrps2, + method = "rf", + tuneLength =...

...r set. # code rm(list=ls()) library(caret) # generating response and design matrix X<-matrix(rnorm(50*100),nrow=50) y<-rnorm(50*1) # Applying caret package con<-trainControl(method="cv",number=10) data<-NULL data<- train(X,y, "lasso", metric="RMSE",tuneLength = 10, trControl = con) coefs<-predict(data$finalModel,s=data$bestTune$.fraction, type ="coefficients", mode ="fraction")$coef coefs *This is the output which I got :* you can see some of the predictors are missing like V4, V6, V7 V1 V2 V3...

...time I run train I get this message: "undefined columns selected" I copy the syntax: fitControl <- trainControl(method = "cv", number = 10, classProbs = TRUE,returnResamp = "all", verboseIter = FALSE) glmFit <- train(Descr, Categ, method = "glmStepAIC",tuneLength = 4,trControl = fitControl) Thank you. Best regards, Jon Toledo, MD Postdoctoral fellow University of Pennsylvania School of Medicine Center for Neurodegenerative Disease Research 3600 Spruce Street 3rd Floor Maloney Building Philadelphia, Pa 19104 [[alternative HTML version deleted...

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks, James...

...1)] test_t<-testset[,-ncol(testset)] species_test<-as.factor(testset[,ncol(testset)]) test_t<-test_t[,-(1)] #### #CARET::TRAIN #### fit1<-train(train1,as.factor(trainset[,ncol(trainset)]),"svmpoly",trControl = trainControl((method = "cv"),10,verboseIter = F),tuneLength=3) pred<-predict(fit1,test_t) t_train[[i]]<-table(predicted=pred,observed=testset[,ncol(testset)]) tune_result[[i]]<-fit1$results; tune_best<-fit1$bestTune; scale1[i]<-tune_best[[3]] degree[i]<-tune_best[[2]] c1[i]<-tune_best[[1]] } -- View this message in context:...

...;) ## data needed for SVM with RBF: ## Not run: tmp <- iris names(tmp)[5] <- ".outcome" head(tmp) createGrid("svmRadial", data = tmp, len = 4) ## End(Not run) What I am doing wrong? Also, what is the connection between len above and tuneLength in the argument for train? Thanks, James [[alternative HTML version deleted]]

...tation. The data is about 20,000 rows and a few dozen columns, and the categories are quite asymmetrical, 4.1% in one category and 95.9% in the other. When I train a ctree model as: model <- train(dat.dts, dat.dts.class, method='ctree', tuneLength=8, trControl=trainControl(number = 5, workers=1), metric='Kappa') I get the following puzzling numbers: mincriterion Accuracy Kappa Accuracy SD Kappa SD 0.01 0.961 0.0609 0.00151 0.0264 0.15 0.962 0.049 0.0011...

...n 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 you! -- View this...

I looked at the documentation of source() and summary(), and I could not find the reason why calling something like: > summary(resamps) from the command line, works (it prints the summary) whereas calling summary(resampls) from a file that I source with source("my_file.r") does not print anything. How can I get summary(resamps) to print when I source a file with this command?

...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, ...) : At least one of the class levels are not valid R variables names; This m...

...#39;)) #Create bootstrap samples for fitting models library(caret) print("Creating bootstrap samples") tmp <- createResample(Y,times = 25) myCtrl <- trainControl(method = "boot", index = tmp, timingSamps = 10) RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) NNmodel <- train(X,Y,method='nnet',trControl=myCtrl,tuneLength=3, trace = FALSE) ## GLMnet = GLMmodel, ... #Assess re-sampled (out of sample) accuracy print("Assessing re-sampled (OOB) accuracy") resamps <- resamples( list( RF = RFmodel,...

...ht 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?, ?O32089?, ?O32663?, ?O32668?, ?O32676? No...

...I want to train my model with LASSO using caret package (glmnet). So, in glmnet, there are two parameters, alpha and lambda. How can I fix my alpha=1 to get a lasso model? con<-trainControl(method="cv",number=10) model <- train(X, y, "glmnet", metric="RMSE",tuneLength = 10, trControl = con) Thanks Alex Roy [[alternative HTML version deleted]]

...) 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"), metric = "cRand", tuneLength = 4) svmNew ------------------- here is an example on how to train the ksvm with spectral kernel ------------------- # Load the data data(reuters) y <- rlabels x <- reuters sk <- stringdot(type="spectrum", length=4, normalized=TRUE) svp <- ksvm(x,y,kernel=sk,scale=c(),cros...

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled 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 =

...essage:> > 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 ea...