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Dividing the data into training,t​esting,val​idation

Asked by FIR
on 6 Sep 2012
Latest activity Edited by Greg Heath
on 15 Jun 2016

I have a dataset of 75x6,in which i want to divide the data into training ,testing and validation and use rbf neural network to classify them,please tell how to divide and classify using rbfneural network

i used newrbe for training and testing before ,but how to include validation data in it

for reference

please help




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1 Answer

Answer by Greg Heath
on 7 Sep 2012
Edited by Greg Heath
on 15 Jun 2016
 Accepted answer

>> lookfor divide


divideblock - Partition indices into three sets using blocks of indices.

divideind - Partition indices into three sets using specified indices.

divideint - Partition indices into three sets using interleaved indices.

dividerand - Partition indices into three sets using random indices.

dividetrain - Partition indices into training set only.

dividevec - Divide problem vectors into training, validation and test vectors.

>> help divideblock, doc divideblock ...

To use a function like newrbe with divided data:

1. Use the training design data to create several (10?) nets with different spread values.

2. Use the validation training set to choose the best net.

3. Return to 1 if you want to refine your search for an optimal spread value

4. Use the nondesign test set to predict performance on unseen nondesign data.

5. If the result is unsatifactory

 a. In order to reduce the bias of future test set predictions, 
obtain a new division of the data (perhaps with differet percentages).
 b. Return to step 1

Hope this helps

Thank you for accepting my answer.



on 12 Sep 2012

I tried as per your code

net = newrb(x1,groups)

y = sim(net,xtst);


Undefined function or variable 'plotFcn'.

Greg Heath
on 13 Sep 2012

Reread my instructions

Do not enter the command plotFcn.


Enter the command net without the ending semicolon. Then look for plotFcn.


Enter the command


In fact, do both so that you will understand

on 14 Sep 2012

Thanks i understood now

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