Hi guys!
I want to use Neural Networks (command-line functions) for a classification problem with currently 15 features and 2 (or maybe 3) different target classes.
1) Am I right that for this kind of problem it would be wise to choose "patternnet" instead of "feedforwardnet"? When they speak about "function fitting" in the documentation, the network will output a (real) value instead of a class, right?
2) I want to test parameters for the network to see how I can adapt it accurately on the given situation. I thought about varying the following parameters:
- number of layers and sizes (net.numlayers is always hidden layers+output layer, right?)
- the training function, maybe to trainlm, trainscg, trainbr
- number of epochs
- transferfunction
- outputlayer transfer function (does that make sense??)
What about the learning rate? Could not find that in net.trainParam.
Does that make sense like this? Any parameters with a big influence I forgot or unuseful ones listed?
So far for now, thanks a lot! Jay
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