Hi @jpol ,
Sure, not a problem. Attached to this post is the modified Powder Metallurgy.jmp file from their sample data.
Open up the JMP data table and the JSL file (be sure to be in the data table when running the JSL or alternatively, be sure to select the correct data table used for modeling from the pull-down menu in the JSL window -- see screenshot).
Click on the "Select Data Table" button to select the data table -- this is needed to feed column names to the next window. Select table and click "OK".
Select the modeling method of interest, in this case Bootstrap Forest, then click "OK". Note, the manual XGBoost modeling requires you to generate a manual tuning table and should probably be done only if you are quite familiar in how to do this for XGBoost.
In the next window, you will cast columns into selected roles, set up tuning table parameters, and DOE options. Enter in the values you see in the screen shot below and use 1234 for the random seed for fitting and for the DOE, this should generate the exact same fit table that I have for this post. Be sure to deselect "Informative Missing" and "Ordinal Restricts Order". Then click "OK".
Note, since we are using the Validation Column, the validation method in this window is automatically selected to "Validation Column". If you didn't want to use a validation column, but a holdback portion, leave the validation column selection empty when casting columns into selected roles and enter a number between 0 to 1 when you select the radio button for the validation method. The default is 0.3 (30% of the data) if you accidentally leave it blank.
There's also some notes on setting tuning parameters for each modeling method in their respective tune setup windows. These are just a guide and some "rule of thumb" usage and should not be considered as the only possible options.
Several windows will then popup and disappear -- this is the code generating the DOE tuning table used to pass to the fit platform, and then you should get a JMP window telling you the fit progress -- for the immediate fit. There will also be a small window that pops up giving you the overall fit progress.
Note that even though you put in 10 runs for the DOE, there are 11 total (N+1) because I programmed it to always run a fit using the platform's default settings within JMP. After the fit is done, you should get a window like the one below. You can then use the right/left arrows to cycle through the different runs and tuning parameters while looking at the R^2 fit statistics (and the difference between training R^2 and fit R^2).
Using the table to the right of the graph, select the run that you would like to re-do. If the the "Save results data table" is checked, then you will be prompted to give the table a name and location to save it. The program will re-run just that one fit and display the results. Here, we're re-running Tune 6. I'm also attaching the output data table from the tuning to this post so you can compare with your use.
You should now get a window like such:
Note that the results window from the original tune is still open, this is so you can re-run other tune runs on an individual basis and compare them if desired. You should get the same results as above if you used all the same numbers in the model tuning. After you re-run individual fits, you can of course use the red hot-button to save those scripts to the data table -- it will populate all the parameters for the fit and save the script to re-run the bootstrap forest fit platform to the data table.
Once you are done, you can click "Cancel" to exit the JSL code, or click "Relaunch" to get back to the first window of the JSL code where you can select other data tables and/or fitting platforms.
I'm also attaching the JSL code if you need it.
Hope this helps, and let me know if you have any questions.
Thanks!,
DS