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konstantin
konstantin
Level III

JMP Support Vector Machines (SVM) platform

Created: Jan 30, 2020 04:12 PM  |  Last Modified: Jan 30, 2020 1:13 PM (6496 views)

I am trying to recreate SVM model I did in R but now using JMP builtin SVM platform. Is it possible to do JMP SVM without scaling and centering of my input parameters? I have chemical mixture components expressed in weight fractions so all of them have the same units (wt.fractions). I also wonder if JMP SVM can optimize C and Gamma or I need to do it in R? I am using JMP Pro 15.0.

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11 REPLIES 11
SDF1
Super User SDF1
Super User

Re: JMP Support Vector Machines (SVM) platform

Mar 4, 2020 04:42 PM (790 views)  |  Posted in reply to message from Mark_Bailey 03-04-2020
True, but you used the NN platform to estimate the optimal values for cost and gamma in order to achieve the best model fit given the possible combinations you used in the for loops, right?

My question is just about whether or not there is a benefit of using the NN platform over Gaussian processes to find the optimal values.
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Mark_Bailey
Staff Mark_Bailey
Staff

Re: JMP Support Vector Machines (SVM) platform

Mar 4, 2020 05:01 PM (786 views)  |  Posted in reply to message from SDF1 03-04-2020

Can't say.

 

I just used Neural because it is a flexible interpolator. It served my purpose. I did not mean to imply that it is the BEST interpolator in this case or any other case in general. I got my benefit. I did not consider any other interpolator. Another one might have done better in this case, but this result was 'gut genug.'

 

The choice of the best model ALWAYS DEPENDS. It isn't 'plug and chug.'

 

Many niches of predictive modeling have their favorite model and never look at other models. Maybe it was the best model the first time that a model was trained. Well, data changes and so should the predictive model. The characteristics of the data determine the choice of the best type of model type but the characteristics of the data change over time. So we need to refresh the model as the data changes and probably need to re-evaluate the choice of the type of model, too. (We won't get into ensemble models today.)

 

A reply to a question in this discussion is incapable of explaining all the pros and cons of all the types of predictive models in different situations. No type of model is superior. I suggest reading "Elements of Statistical Learning."