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Dear members,

I need to know how to determine the type of cross validation in neural networks node in enterprise miner?

thanks

1 ACCEPTED SOLUTION

Accepted Solutions
M_Maldonado
Barite | Level 11

Hi Hussein,

What do you mean by "type of cross-validation" in neural networks?

The Start/End group nodes support cross validation for any model. For a quick example, take a look at page 7 of this paper (http://support.sas.com/resources/papers/proceedings10/123-2010.pdf)...

I can't remember top of my head if you have that option in EM 6.2. If your EM version does not have cross validation option for the Start groups, I suggest go back to the basics. When I read the wikipedia definition of k-fold validation, it sounds like something easy to do in EM. Just an idea, use several Sample nodes, add the same model node to all of them, and use an Ensemble node to combine the posterior predicted probabilities. I have not ran this, but that would be my first try.

Please let me know if this worked OK.

Thanks!

Miguel

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4 REPLIES 4
mohamed_zaki
Barite | Level 11

What is your version of EM ?

I have found other people to refer to the lack of the cross validation in EM with neural network, so they repeat the test several time with different data partition seeds and taking the average.

Check this:

http://www.kms.pwr.wroc.pl/staff/trawinski/wp-content/uploads/2013/09/2009-Comparative-Analysis-of-N...

Hope other to  tell us if that possible.

husseinmazaar
Quartz | Level 8

The version of Enterprise Miner is 6.2. Any way around ?

Thanks

M_Maldonado
Barite | Level 11

Hi Hussein,

What do you mean by "type of cross-validation" in neural networks?

The Start/End group nodes support cross validation for any model. For a quick example, take a look at page 7 of this paper (http://support.sas.com/resources/papers/proceedings10/123-2010.pdf)...

I can't remember top of my head if you have that option in EM 6.2. If your EM version does not have cross validation option for the Start groups, I suggest go back to the basics. When I read the wikipedia definition of k-fold validation, it sounds like something easy to do in EM. Just an idea, use several Sample nodes, add the same model node to all of them, and use an Ensemble node to combine the posterior predicted probabilities. I have not ran this, but that would be my first try.

Please let me know if this worked OK.

Thanks!

Miguel

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