What inspired this wish list request?
I have been working on predictive models where I have my data set split in to defined inputs and a validation split, but I have up to 9 response parameters I am trying to model. I noticed if I make a PLSR model I can add all the response parameters to the response box in the gui and the software will make 9 independent models (one for each response parameter) summarized together in the results. But when using an algorithm like a NNET I can either model each response parameter one at a time, or if I had them all to the response box in the gui instead of JMP speeding up the process and training a NNET for each parameter, it trains a single combined NNET model to try and predict each output. It simply is not clear when the software is making independent models or fusion model.
What is the improvement you would like to see?
I think there are two additions that user would find helpful.
- Updating instructions in the how tos
- It would be good to have it clarified in the help/how to section how JMP treats modelling multiple response.
- Adding a radial option in model gui for the applicable algorithms.
- I think the easies option would be to include a radial option on the amenable algorithms (NNET and Tree-based etc.) that would let the user select if they want all the response parameters to be modelled collectively vs. independently.
- I think there was an option similar to this for how jmp handles multiple response parameters back in JMP 13 (maybe still included in JMP 15) but its an option that seem to have been removed.
Why is this idea important?
I think this idea is important for two reasons.
- First is simple clarification. It wasn't immediately evident how some of the algorithms like NNET treat multiple response parameters in JMP and I think users would benefit from a more straightforward description of how modelling multiple responses in JMP works so they know the software is doing what is expected.
- Additionally I think this option, just gives users more flexibility in how they use JMP efficiently and would allow them to maximize the value they gain from JMP. In my experience their are applications where modelling results together vs. independently can be critical to maximize model performance.