This is my first post in CrossValidated hence please let me know if I may have inadvertently violated forum rules.
I am working with nnet using Caret in R and when I am running experiments using the tuning grid I am somehow not able to get any results with size = 8 and above.
My code is as follows:
set.seed(seedVal) ### creating a grid of tuning parameters nnetTunegrid <- expand.grid(.size = seq(min_tune,max_tune,step_tune), .decay = seq(0,4,0.125)) # set seeds array for cross validation seeds <- setSeeds(cv_count, cv_repeats, nrow(nnetTunegrid), seedVal) # Define cross-validation experiment numFolds = trainControl(method = "cv", number = cv_count, #repeats = cv_repeats, seeds = seeds, classProbs = TRUE, summaryFunction = twoClassSummary) registerDoParallel(cores = 6) nnetFit <- train(x = train_matrix, y = catg_labels, method = "nnet", preProc = preProcessing, trControl = numFolds, tuneGrid = nnetTunegrid, maxit = 500, # max iterations for nnet only metric = metricVal)
My data set has 150 features and I am using nnet to do binary classification.
Any help or pointers to resolve this problem would be appreciated!