I am using the neural platform to make predictions, and, being new to neural networking, I am only slightly familiar with all the input parameters: number of hidden layers, number of nodes/hidden layer, boosting models, learning rate, and tours. What I want to do is try to minimize RMSE and the validation model misclassification rate. What I've been doing is iteratively changing each parameter one by one, saving the model performance parameters, and pasting them into a new JMP table, but this is going to take days since there are so many combinations of layers, nodes, tours, etc. Would it be possible to write a script to where JMP Pro builds, say, 1,000 models and dumps the data into a table so that I don't have to manually change each model input parameter?
#Hidden layers: 1 or 2
#Sigmoidal nodes: 0, 1, 2, 3, or 4
#Linear nodes: 0, 1, 2, 3, or 4
#Radial nodes: 0, 1, 2, 3, or 4
#boosting models: no idea, 1-5 maybe?
#learning rate: 0-0.5 in 0.1 increments
#tours: 1, 5, 10, 20, 100
So that would be 2 x 5 x 5 x 5 x 5 x 5 x 5=~30,000.
I guess I could widdle a few of these down once I am more familiar with the dataset. Of course, having many nodes and a high number of boosting models doesn't make sense (nor do certain other combinations), but we're still talking about potentially hundreds of models worth testing. Surely this sort of model screening/comparing could be scripted, right?
Anderson B. Mayfield