Terrific! Thanks for the speedy and informative reply.
--JJS ________________________________ From: William Dunlap <wdun...@tibco.com> Sent: Friday, August 17, 2018 12:45 PM To: Sparks, John Cc: r-help@r-project.org Subject: Re: [R] CARET NN Too Much Output Even with Trace=False You can use capture.output to store all that tracing information in a character vector instead of having it printed. You can look at it to diagnose problems or just throw it away. NN.text <- capture.output(NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet',trControl=fitControl,tuneLength=5,nnet.trace=FALSE)) Bill Dunlap TIBCO Software wdunlap tibco.com<http://tibco.com> On Fri, Aug 17, 2018 at 10:34 AM, Sparks, John <jspa...@uic.edu<mailto:jspa...@uic.edu>> wrote: Hi R Helpers, I am using the Neural Net build in the CARET package and it produces a large amount of output that I don't need to see and interferes with my ability to get to the output that I want to see. I am using the nnet.trace=FALSE setting, but still getting a disproportionate amount of output from this one procedure. Is there another option setting that will turn off this output? Reproducible example is below. It has a little extra complication in it because I hacked it from a post. Let me know if I need to do anything to it to make it more use-able. Many thanks. --John Sparks library('caret') set.seed(1) data<-read.csv(url('https://datahack-prod.s3.ap-south-1.amazonaws.com/train_file/train_u6lujuX_CVtuZ9i.csv')) #Imputing missing values using median preProcValues <- preProcess(data, method = c("medianImpute","center","scale")) library('RANN') data_processed <- predict(preProcValues, data) index <- createDataPartition(data_processed$Loan_Status, p=0.75, list=FALSE) trainSet <- data_processed[ index,] testSet <- data_processed[-index,] fitControl <- trainControl(method = "cv",number = 5,savePredictions = 'final',classProbs = T) trainSet<-subset(trainSet,select=-c(Loan_ID)) outcomeName<-"Loan_Status" predictors<-names(trainSet)[!names(trainSet) %in% outcomeName] NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet',trControl=fitControl,tuneLength=5,nnet.trace=FALSE) [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org<mailto:R-help@r-project.org> mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
