Hi all, I have considered neural network to classify the health status of the cow. I found a very neatly written R codes for classification method in here<http://home.strw.leidenuniv.nl/~jarle/IAC/RRoutines/classification-example.R> .
It would be very helpful if you can answer some of the questions, that I am struggling with, I have set of time series data from different animals, I use data on 10 animals to train the model and 1 animal to test the model at a time. Below is the simple Rcode , I used before complicating myself. But the prediction resulted in classifying absolute 0 and 1, even though I have asked results in integer value(#round()). - In section 6.2 of neural network and related method for classification, by B.D. Ripley<http://www.jstor.org/stable/2346118?origin=JSTOR-pdf>it is specified as below. [image: Inline image 9] I tried to transfer my data in the range [0,1] before feeding them to nnet. But it does not seemed to improve my result. - I would like to know, on specifying size of the hidden layer and decay value. - How important is it specifying linout=T in the function? I want output in terms probability, so I have not specified it!!!!!!!! - In Rcode mensioned above, I see r$data, in all the places, I just wonder whether training and test data are considered as same!!! #---------------------------------------------------------------------------------- ## training NNET Nnet <- nnet(x=data, y=class.ind(dat$trt_window), size=2,rang=0.05,skip=T, decay=0.000001, maxit=500)# ## validation for each lactation pred <- predict(Nnet, newdata=y, type = "raw") #----------------------------------------------------------------------------------- Your help is very much appreciated, Thanks for your time advance. [[alternative HTML version deleted]]
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