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)
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