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Neural Network Tuning


November 27, 2023 - Version 1.1 - improvements and bug fixes, details are in a post below

November 28, 2023 - Version 1.11 - minor bug fix

December 15, 2023 - Version 1.2 - improvements and bug fixes, details are in a post below



The Neural Network Tuning add-in is an alternative Neural Network platform that provides an easy way to generate numerous neural networks to identify the best set of parameters for the hidden layer structure, boosting, and fitting options.


How it works

The user specifies the number of Neural Network models that will be run and sets the range of parameters and fitting options. A Fast Flexible Space Filling DOE is generated with the tuning parameters as Factors, and the Training, Validation, and Testing R2 values as Responses.


Each trial in the DOE is passed to the Neural Network platform and the R2 values are recorded in an output DOE table. After all models are run, the table is sorted by Validation R2 and a series of graphs are displayed to help the user identify the most important effects for maximizing the Validation R2 values.


Additional models can be generated by adjusting the tuning parameters and re-running the DOE. The new runs will be appended to the original table.


The data table can be saved and re-loaded into the platform to save progress and re-start additional analysis. This is a way to tune Neural Networks that might take considerable computation time.


Video Guide




The add-in has been tested on Windows and Mac using JMP Pro Version 17.1 and 17.2.


Known Issues

- The add-in does not currently handle a Response (Y) column that is virtually linked to the factor data table. As a work-around, you can unhide the linked column and then cut/paste the data into a new column.


Hi Scott,

Very handy add-in. Previously I had to manually generate a space filling design to try and explore the NN platform. Will give it a test run! Any plans to expand the number of layers in the NN platform? Also any chance tuning can be implemented in the genreg platform too? 




Hi, @Vins!.


Just wanted to chime in on this.  GenReg can be tuned already, just set the alpha hyper-parameter to "." and JMP will optimize it.  On more layers in the NN, that's more in the hands of JMP Development than Scott.  You should put the request in the JMP Wishlist so that they know you'd like to see deeper NN's available.  





@Vins -

Right now, the add-in just passes parameters to the JMP NN platform, which builds NNs with up to two layers. If the platform changes (more layers, activation functions, etc.) and there is sufficient interest, I can update the add-in. I want to second @MikeD_Anderson 's suggestion and head over to the wishlist if there are features you would like to see in future JMP versions.

Let me know how your test run goes!



This is great. I've been wondering when something like this would make its way into JMP Pro, but great initiative making this add-in to do it.  +50 points for Gryffindor!

Great tool thank you so much. I've just tetsed it on various datasets, it save me a lot of time and find conditions (parameters) that I would not have necessarily tested for sake of time. Thank you Scott !!

Great work @scott_allen on the nice setup and functionality of this add-in!

Two concerns / warnings:

1. The Neural platform peeks at the validation data while performing its model fitting, and so the validation results are leaky and overly optimistic.   As such, I don't think they provide a good estimate of generalization performance nor a good way to compare different models in the tuning design.   The only way around this I am aware of is to use a three-level Validation column (Training, Validation, and Test, creatable with Analyze > Predictive Modeling > Make Validation Column) and then only use the Test results to compare models.   I just checked that the add-in handles this case, and to verify the general point, plot Validation R2 versus Test R2.

2.  Throwing a lot of models at a single test holdout set will very likely lead to overfitting that test set.   This happened with the Autotune functionality in XGBoost (which uses the same Fast Flexible Design approach), prompting a change to only output an ensemble model when autotuning and to use nesting within each fold to avoid leakiness.  

@Vins  Arbitrarily deep NNs are available in the new Torch Deep Learning add-in; please sign up to try it at JMP 18 Early Adopter    It does not yet have autotuning but may in the future.


Great points. I agree with both and hope to continue developing this Add-In to address these.


My first goal was to automate the current model generation workflow and now I would like to start thinking about including "guard rails" or help find ways to include additional validation methods.


I also like the idea of ensemble models. I am considering the addition of a simple way for the user to pass models to the Compare Models platform. But it might be better to automate this as well based on some model fit statistic. Food for thought!




 @scott_allen I have added to the wish list for more layers to NN.


@russ_wolfinger , I have already taken the Torch  Deep Learning add-in for several test runs in EA5. I do get better models than xgboost add-in, however I always go back to your xgboost add-in I save a table of "gain", "splits","cover", scores for each variable. With this table I select only variables with non 0 values and rerun xgboost with a smaller set of predictors. Usually after 2-3 rounds, I get no non-zero predictors. This way I filter out  unimportant variables, and I can get the profiler to visualise a smaller model. My predictors are routinely 60-300K. I would like to see an option in the  Torch  Deep Learning add-in where I can get access to a table with values I can use to kick out unimportant variables.


Is there going to be an expanded Fast Flexible Design in xgboost that also includes the other ~ 30 advanced options which include categorical parameters? And lastly @russ_wolfinger, is the next adopter EA release going to have a tuning table for the Torch  Deep Learning add-in? cant wait, the models are significantly better!

@Vins   Thanks much for the feedback.  I think your variable reduction approach in XGBoost is reasonable (I've done the same myself) since those variables with 0 importances are not used in any of the models.    Both variable importances and autotuning are on the "to-do" list for Torch.    

XGBoost autotuning over all of the advanced options would require a much larger and messier interface, and we can think about it.  In the meantime, there is a Tuning Design Table option in which you can specify a secondary JMP table with design points, e.g. created by invoking DOE FFF directly on the parameters you want to tune.  

We are also looking more now at Bayesian Optimization approaches.  There is an add-in .


The Neural Network Tuning add-in has been updated. Version 1.1 is now available for download and version 1 has been removed. This version has a number of bug fixes and over-all improvements based on feedback from users:

Bug fixes

  • Informative Missing works.
  • Various warnings are thrown to avoid JMP Error messages when problems are encountered in the column specification or parameter specification steps.
  • Multi-level nominal and ordinal Responses are now supported.


  • General interface updates
  • KFold validation is now available
  • A Help window can be launched by clicking the "Help" button. The Help window has additional information on how Model Validation, Comparing Models, and Saving Models are handled in version 1.1.
  • Model validation is now always carried out with a column to help with model reproducibility.
  • Model Comparison is now carried out using the Compare Model platform in a duplicate (and hidden) data table.
  • Save models directly from the tuning results window using the Save Models button.
  • When a previously saved data table is loaded, the add-in will warn you if the X variables in the saved data table are different than the X variables specified when the add-in was launched.
  • The graph builder output now has a column switcher toggle to look at Training, Validation, or Testing R2 values versus each parameter.
  • Two more model visualization graphs are added as tabs in the results window. These are also interactive with the data table to help identify the best models.
  • Model Progress window has a few updates:
    • Overall modeling time is shown
    • Average time per model is shown
    • A Cancel button is available in the model progress window in case the overall modeling time is longer than desired. Any model that was completed during the modeling step will be saved to the results window. Note that when modeling time is long, this button can "lag" and it may require a few clicks to register the command.
  • Models saved to the data table have an updated Notes section with all the parameters used to generate the model.



Version 1.2 is now available. This version has a number of bug fixes and addition improvements based on feedback from users:

Bug fixes

  • Compare Models will only bring recently prepared NN models into the Compare Models platform instead of all previously generated models
  • Model comparisons with nominal and ordinal Y values now work
  • Other minor fixes


  • You can now recall tuning parameters
  • Added different model saving options
  • Showing the tuning table generates a copy of the table
  • The data table is now saved as a tab in the results window
  • Added unique model IDs to facilitate comparison of models with different random seeds
  • New graph is available when multiple random seeds are used

Hi Scott,

Excellent complement, it is what the NN platform is missing, a query... How do I get it to analyze 2 or more outputs (and)....



Hi @Marco2024 

To analyze two or more variables, you will need to create a model for each and then load the prediction expressions in the Profiler:

  1. Build a NN model for your first output (Y) and save the prediction expression by clicking the "Save models" button. The "Profiler Formulas" option will save a single formula column to your data table.
  2. Repeat this for all the Y-variables you would like to compare.
  3. Launch the Profiler (Graph > Profiler) and load your prediction expressions.

This will provide a profiler for each of your Y variables stacked on top of each other to help you compare and co-optimize your system.