Validation Column Role
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, K-Fold Cross-Validation 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, K-Fold Cross-Validation is used. |
|
Partial Least Squares |
No |
Yes |
Yes |
If there are more than three levels, K-Fold Cross-Validation is used. |
|
Predictive Models |
|
|
|
|
|
Neural |
No |
Yes |
Yes |
If there are more than three levels, K-Fold Cross-Validation 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, K-Fold Cross-Validation 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, K-Fold Cross-Validation is used. |
|
Uplift |
No |
Yes |
Yes |
If there are more than three levels, the platform only uses rows with the three smallest values. |