R中的神经网络

Question

hi i am trying to use neuralnet function in R so i can predict an integer outcome (meaning) using the rest of the variables. here is the code that i have used:

library("neuralnet")

I am going to put 2/3 from the data for neural network learning and the rest for test

ind<-sample(1:nrow(Data),6463,replace=FALSE)

Train<-Data[ind,]

Test<-Data[-ind,]

m <- model.matrix(
  ~meaning + 
    firstLevelAFFIRM + firstLevelDAT.PRSN + firstLevelMODE + 
    firstLevelO.DEF + firstLevelO.INDIV + firstLevelS.AGE.INDIV + 
    secondLevelV.BIN + secondLevelWord1 + secondLevelWord2 + 
    secondLevelWord3 + secondLevelWord4 + thirdLevelP.TYPE,
  data = Train[,-1])  #(the first column is ID , i am not going to use it)

PredictorVariables <- paste("m[," , 3:ncol(m),"]" ,sep="")

Formula <- formula(paste("meaning ~ ", paste(PredictorVariables, collapse=" + ")))

net <- neuralnet(Formula,data=m, hidden=3, threshold=0.05)

m.test < -model.matrix(
  ~meaning + 
    firstLevelAFFIRM + firstLevelDAT.PRSN + firstLevelMODE + 
    firstLevelO.DEF + firstLevelO.INDIV + firstLevelS.AGE.INDIV + 
    secondLevelV.BIN + secondLevelWord1 + secondLevelWord2 + 
    secondLevelWord3 + secondLevelWord4 + thirdLevelP.TYPE,
  data = Test[,-1])

net.results <- compute(net, m.test[,-c(1,2)]) #(first column is ID and the second one is the outcome that i am trying to predict)

output<-cbind(round(net.results$net.result),Test$meaning)

mean(round(net.results$net.result)!=Test$meaning)

the misclassification that i got was around 0.01 which is great, but my question is why the outcome that i got (net.results$net.result) is not an integer?

Answer 1

I assume that your output is linear. Try setting linear.output = FALSE .

net <- neuralnet(Formula, data = m, hidden = 3, threshold = 0.05, linear.output = FALSE)