The Validation Column role is available only in JMP Pro. For JMP, see Excluded Rows as Validation Holdback.
One way to create data partitions is to use the Validation Column role. The Validation Column role uses the column’s values to divide the data into parts. The column is assigned using the Validation role in the platform’s launch window. For information about how to create a validation column, see Make Validation Column.
Caution: The use of a validation column is platform specific. Different platforms use the levels of the validation column differently. See notes in Table A.1.
Table A.1 Validation Column by Platform
Platform 
Train & Evaluate 
Train & Tune 
Train, Tune, & Evaluate 
Notes 

Fit Model 




Fit Least Squares 
Yes 
No 
No 
If there are more than three levels, the validation column is ignored. 
Stepwise Regression 
No 
Yes 
Yes 
If there are more than three levels, KFold CrossValidation is used. 
Logistic Regression 
Yes 
No 
No 
If there are more than three levels, the validation column is ignored. 
Generalized Regression 
No 
Yes 
Yes 
If there are more than three levels, KFold CrossValidation is used. 
Partial Least Squares 
No 
Yes 
Yes 
If there are more than three levels, KFold CrossValidation is used. 
Predictive Models 




Neural 
No 
Yes 
Yes 
If there are more than three levels, KFold CrossValidation is used. 
Partition 
No 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 
Bootstrap Forest 
No 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 
Boosted Tree 
No 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 
K Nearest Neighbors 
No 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 
Naive Bayes 
No 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 
Support Vector Machines 
Yes 
Yes 
Yes 
If there are more than three levels, KFold CrossValidation is used. 
Specialized Models 




Functional Data Explorer 
Yes 
No 
No 
Must be created as a Grouped Random validation column. If there are more than two levels, the smallest value defines the training set and all other values define the validation set. 
Multivariate Models 




Discriminant 
Yes 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 
Partial Least Squares 
No 
Yes 
Yes 
If there are more than three levels, KFold CrossValidation is used. 
Uplift 
No 
Yes 
Yes 
If there are more than three levels, the platform only uses rows with the three smallest values. 