Neural network (nnet) with caret and R. Machine learning classification example, includes parallel processing.

nnet.R

library(caret)
library(doParallel)

registerDoParallel(cores = 2)

# Read data.
data <- read.csv('train.csv')
test <- read.csv('test.csv')

# Set classification column to factor.
y <- as.factor(make.names(data$TARGET))

# Remove columns with near zero variance.
nzv <- nearZeroVar(data)
data <- data[,-nzv]
test <- test[,-nzv]

data$TARGET <- y

##### Removing constant features
cat("\n## Removing the constants features.\n")
for (f in names(data)) {
  if (length(unique(data[[f]])) == 1) {
    cat(f, "is constant in train. We delete it.\n")
    data[[f]] <- NULL
    test[[f]] <- NULL
  }
}

##### Removing identical features
features_pair <- combn(names(data), 2, simplify = F)
toRemove <- c()
for(pair in features_pair) {
  f1 <- pair[1]
  f2 <- pair[2]
  
  if (!(f1 %in% toRemove) & !(f2 %in% toRemove)) {
    if (all(data[[f1]] == data[[f2]])) {
      cat(f1, "and", f2, "are equals.\n")
      toRemove <- c(toRemove, f2)
    }
  }
}
feature.names <- setdiff(names(data), toRemove)
data <- data[, feature.names]
test <- test[, feature.names[feature.names != 'TARGET']]

inTrain <- createDataPartition(data$TARGET, p = 3/4)[[1]]
training <- data[inTrain,]
testing <- data[-inTrain,]

# Train on entire training set.
# training <- data

numFolds <- trainControl(method = 'cv', number = 10, classProbs = TRUE, verboseIter = TRUE, summaryFunction = twoClassSummary, preProcOptions = list(thresh = 0.75, ICAcomp = 3, k = 5))
fit2 <- train(TARGET ~ . -TARGET -ID, data = training, method = 'nnet', preProcess = c('center', 'scale'), trControl = numFolds, tuneGrid=expand.grid(size=c(10), decay=c(0.1)))

results1 <- predict(fit2, newdata=training)
conf1 <- confusionMatrix(results1, training$TARGET)

results2 <- predict(fit2, newdata=testing)
conf2 <- confusionMatrix(results2, testing$TARGET)

probs <- predict(fit2, newdata=test, type='prob')

# Assemble output format: ID, prob.
output <- data.frame(ID=test$ID)
output <- cbind(output, TARGET=probs$X1)
write.csv(output, file='output.csv', row.names=FALSE, quote=FALSE)

where should I received the 'test.csv','train.csv' file?
-Thanks

for the tune parameter:size, decay, how do you choose these parameters?

When I use nnet to predict house selling price, all the response are "1", seems like a classifying output. Do you have any ideas on how to fix it?

use linout=1 for regression
linout=0 for classification

this may help
https://stats.stackexchange.com/questions/21717/how-to-train-and-validate-a-neural-network-model-in-r

how to I reuse a neural network model after training and testing the performance of the NN inorder to predict for new unknown output

how do I*