JMP provides K-fold cross-validation in some platforms but not all of them. You would have to implement your own version of it. Here is one way to do it:
- Create a new data column called Fold that uses the Nominal modeling type and with a formula of Random Integer( 1, 5 ). This column will identify the five folds for you.
- Create a series of five more data columns called Validation i, also using the Nominal modeling type, and each with a formula of Fold == i, where i changes from 1 to 5 as you go from the first to the last of these new columns.
- Launch the Discriminant platform five times using the succession of Validation i columns in the Validation analysis role.
You should have new columns for 5-fold cross-validation in your data table like this.
![Capture 1.JPG Capture 1.JPG](https://community.jmp.com/t5/image/serverpage/image-id/24214i202875B47271E164/image-size/large?v=v2&px=999)
This first iteration of your model fitting might look like this:
![Capture 2.JPG Capture 2.JPG](https://community.jmp.com/t5/image/serverpage/image-id/24215i0AF4A76358625C82/image-size/large?v=v2&px=999)
Use the cross-validation information for each of the folds, such as:
![Capture 3.JPG Capture 3.JPG](https://community.jmp.com/t5/image/serverpage/image-id/24216iB4DA2D7E42493DCA/image-size/large?v=v2&px=999)
You can then combine the five sets of results into the overall training and validation results as you see fit such as described in your cited reference.