But if you use Anaconda to install things, JMP has problems talking to it when it's greater than 3.6 so Now, of course, with Python be a little bit careful in that the newest version of Python 3.8.5. I'm not going to step through all the lines of code here but you get the basic idea. So here the picture shows I fit the linear regression with Python. ![]() One thing to note so I'm using R 3.6.3 but JMP can handle anything as long as it's greater than 2.9.Īnd then similarly, Python, you can call your own Python session. I'll work a lot more with that data set later.īut if you wanted to just very quickly fit a linear regression model in R and spit out the predictive values, you can do that. So this picture here just shows very quickly if I wanted to fit a linear regression model to some output ![]() OK, so the biggest reason I chose R, Python and MATLAB to focus on for this talk is that turns out JMP and scripting language can actually create their own sessions of R and run code from it. You're going to need something that tells all the software which is training, which is validation, which is test, because you're going to want those to be consistent when you're comparing the fits. So the two big things you're going to need are the model predictions from whatever software you use to fit the model.Īnd generally, when we do model fitting, particularly with larger models, you may split the data into training validation and/or test sets. So we can use the model comparison platform, as I've said, to compare from other software as well. Now that being said, the ideas I'm going to discuss here, you want to go fit them in C++ or Java or Rust or whatever other languageĬomes to mind, you should be able to use a lot of those. So there are a few things unfortunately JMP can't do.īut I'm going to focus on those three. Another one is called multivariate adaptive regression splines. Other activation functions than what's offered by JMP so JMP's not going to be able to do a whole lot of that within JMP.Īnother one is something called projection pursuit regression. Those that do a lot of machine learning and AI might fit something like an auto encoder or convolutional neural network that generally requires lots of activation functions or yes, lots of hidden layers nodes, So just give a few ideas of some things that can't fit. So that being said, JMP can fit a lot of things, but, alas, it can't fit everything. It's very quick and easy to tell, is this better that that, so on so forth. Okay, you could flip back and forth between the two. So if you have a tree and a neural network and you're not really sure which one's better. Model fits from various platforms within JMP. The nonlinear platform for non linear modeling and there's several more.Īnd so within JMP 15 I think it came out in 12 or 13 but this model comparison platform is a very nifty tool that you can use to compare If you want classification and regression trees. There's the neural platform on neural network partition platform. ![]() Okay so currently JMP 15 Pro is the latest and greatest that JMP has out and if you want to fit the model and they've got several different tools to do that. I'm a statistician at Intel.Īnd today I'm going to talk about using JMP to compare models from various environments. ![]() Okay, thanks everyone for coming and listen to my talk.
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