On 10/12/10 02:56:13, jothy wrote:> Am working on neural network.
> Below is the coding and the output [...]
> > summary (uplift.nn)
>
> a 3-3-1 network with 16 weights
>
> options were -
>
> b->h1 i1->h1 i2->h1 i3->h1
> 16.64 6.62 149.93 2.24
> b->h2 i1->h2 i2->h2 i3->h2
> -42.79 -17.40 -507.50 -5.14
> b->h3 i1->h3 i2->h3 i3->h3
> 3.45 1.87 18.89 0.61
> b->o h1->o h2->o h3->o
> 402.81 41.29 236.76 6.06
> Q1: How to interpret the above output
The summary above is the list of internal weights that were learnt during
the neural network training in nnet(). From my point of view I wouldn't
really try to interpret any meaning into those weights, especially if you
have multiple predictor variables.
> Q2: My objective is to know the contribution of each independent variable.
You may try something like variable importance approaches (VI) or feature
selection approaches.
1) In VI you have a training and test set as in normal cross-validation.
You train your network on the training set. You use the trained network
for predicting the test values. The clue in VI then is to pick one
variable at a time, permute its values in the test set only (!) and see
how much the prediction error deviates from the original prediction error
on the unpermuted test set. Repeat this a lot of times to get a
meaningful output and also be sure to use a lot of cross-validation
permutations. The more the prediction error rises, the more important the
respective variable was/is. This approach includes interactions between
variables.
2) feature selection is essentially an exhaustive approach which tries
every possible subset of your predictors, trains a network and sees what
the prediction error is. The subset which is best (lowest error) is then
chosen in the end. It normally (as a side-effect) also gives you something
like an importance ranking of the variables when using backward or forward
feature selection. But be careful of interactions between variables.
> Q3: Which package of neural network provides the AIC or BIC values
You may try training with the multinom() function, as pointed out in
msg09297:
http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg09297.html
I hope I could point out some keywords and places to look at.
Regards,
Georg.
--
Research Assistant
Otto-von-Guericke-Universit?t Magdeburg
research at georgruss.de
http://research.georgruss.de