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rule-extraction algorithms for verify the Artificial Neural Network (ANN)
Hello,
I recently start to use JMP Pro also for the artificial neural network.
I would like to know, how can I verify the neural network architectures with JMP?
E.g., using the mathematical expressions, symbolic logic, fuzzy logic, or decision trees...
I read that this system could resolve the "black box."
Thank you so much,
Angelo
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
Hello dan,
Excellent explanation!!! thanks, I'm going to try your solution, in relation to the price of the shares (attached table 1 base and table 2 objective), I have 4 columns of 74,547 rows with open, high, low and close prices.... how can I convert with a formula in JMP ...... 400 or more new columns that have the maximum possible number of rows with labels and data created by the formula...for example:
open100, high100, low100, close100, open99, high99, low99, close99, open98, high98, low98, close98 ....
Greetings,
Marco
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
Hello dan,
I tried your 200 column solution and I think I'm doing something wrong, I've attached the table with the formula to know what I'm doing wrong, thanks!
Cheers,
Marco
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
Thank you, @Dan_Obermiller, for answering Marco's questions. I would also like to add the white paper by Chris Gotwalt, who manages the JMP statistical developers group.
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
I have further information from JMP Development about your inquiry. They say that it is difficult to verify neural networks (NN) from any software product. NN are not like linear regression where the coefficients and predictions will be the same no matter what product you use. Every product will be very different. The best you can do is use crossvalidation to assess the predictive performance of the data mining models. If there is a strong need to verify the models that are used then stay with least squares and logistic regression.
I included a JMP white paper that addresses as much as we can say about NN in JMP.
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
@AngeloF88 wrote:
Is very easy to do this simulation in JMP. If you know, what is in JMP the difference, between a random tables and simulation from the profile?, are bolt two type of Montecarlo simulation?
Please clarify the tables ("random tables" and "simulation from profile") to which you are referring before I can answer your question.
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
Hi,
Thank you so much for reply me!
I read the JMP documentation, section prediction profiler Options. And I found that "Output random table" is the filtered Montecarlo technique, while the "Simulation" is just a Montecarlo simulation that uses the random noise.
I do not know the difference between the two techniques and what method is better, I have to study better this two difference. If you have some advice , it is well appreciated. However, I have checked that Montecarlo simulation takes more time than filtered Montecarlo technique.
I have to do this in the profile of the neural network. Therefore I thought that it could be a Hybrid ANN-Montecarlo simulation.
To help to understand:
For random table,
1. I selected the option neural
2. I clicked the red triangle in the model
3. I clicked the option Prediction profile
4. I clicked the red triangle in prediction profile.
5. I selected Output random tables.
For simulation, I performed the same procedure, but instead of clicking the "Output random tables", I selected the option "simulator". I clicked the red triangle and selected "Simulation Experiment".
I adjusted the noise in the interactive cells and selected the random variable.
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Re: rule-extraction algorithms for verify the Artificial Neural Network (ANN)
This quote is from the help portion about the prediction profiler command to output a random table:
So it is a simple way to obtain a uniform random simulation of the predictors and the model prediction of the response. It has the same purpose as the Output Grid Table command.
The Simulator function gives you much more control over the nature of the variation of the predictors and additional random variation of the response. It is primarily for assessing process capability but obviously has other applications.
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