Caret retraining of neural network after finding optimal parameters?

Question

I am applying a neural network and logistic regression to a classification problem. In order to evaluate the performance of the two classifiers I'm using 5-fold cross-validation (roughly 800 samples in the test data for each fold). Using caret I am finding the best parameters for my ANN in an inner loop in the cross-validation (roughly 600 samples in test data for each fold). However, I would really like to get a better understanding of how caret handles this.

Seeing as neural networks can benefit from retraining in order to avoid local minima I would like to do some retraining. I am not sure whether caret does this after having selected the optimal parameters or simply trains the model once on the larger sample of test data (800 samples) with the found parameters. Can anyone clarify this for me?

EDIT: And if it doesn't do any retraining, how can I do this in the best possible way?

Code:

              library(caret)
              library(nnet)
              tunGrid <-  expand.grid(size  = c(1, 2, 3, 4, 5, 6, 7),
                      decay = c(0, 10^(-2), 10^(-3), 10^(-4)))

              fitControl <- trainControl(method = "repeatedcv", number = 5, repeats=3)
              set.seed(825)
              fmla <- as.formula("y_BBR_train ~ .")  
              annFit1 <- train(fmla, data = X_BBR_train,
              method = "nnet",
              trControl = fitControl,
              tuneGrid  = tunGrid)

Thank you!

Answer 1

Let's say you have 800 samples. For each fold, you are leaving out about 160 samples and doing this 30 times. For each combination of weight decay and number of hidden units, train will use the average of the 30 resamples to figure out which is empirically best. Once that is finished, it takes those optimal settings and fits a final model with all 800 samples and that is used for future prediction.

I'm not sure if that qualifies as 'retraining' or not. You can also use model averaging with neural networks by using method = "avNNet" if you think that helps.

Max