Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know how to do it in R. Is 'training' and 'training.reports' in (AMORE) able to do it? Thank you for any light on this. Ilh
train nnet
2 messages · G Ilhamto, Max Kuhn
On Dec 14, 2007 10:52 AM, G Ilhamto <gilhamto at gmail.com> wrote:
Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200)
nnet will estimate model parameters for the model that you have specified.
If you want to understand what parameters (e.g. size, decay) should be
used, the train function in the caret package uses a variety of
resampling methods to help pick those parameters (and will refit the
model based on that). There are similar functions in other packages
(like e1071).
Install the caret package and use vignette("caretTrain") to see the details.
Max