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"Number of hidden neurons ANN"
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Hi,
i have a question... of course
The doc for the neural netowrk operators says: the (hidden) layer size will be set to (number of attributes + number of classes) / 2 + 1.
2 question:
1. Why not using 2*num_attrs+1 hidden neurons? With that number is can be guaranteed to approximate any function, as you might know
2. What is the number of classes, if i am making a regression? 1?
Thank you very much
i have a question... of course

The doc for the neural netowrk operators says: the (hidden) layer size will be set to (number of attributes + number of classes) / 2 + 1.
2 question:
1. Why not using 2*num_attrs+1 hidden neurons? With that number is can be guaranteed to approximate any function, as you might know

2. What is the number of classes, if i am making a regression? 1?
Thank you very much
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Answers
well actually beside from asking you could answer some of the questions you already have been solved
1. If you can approximate any function you might really simple overfit. I suggest reading the nice paper "Overfitting S&P 500" at http://www.shookrun.com/documents/stupidmining.pdf
2. Yes, is 1 for regression.
Actually you might add this to the operator documentation, if you would be so kind?
Greetings,
Sebastian