In SAS^® Visual Data Mining and Machine Learning, Model Studio pipelines might generate incorrect results. The problem occurs when these conditions are true:  

• Some of the variables in the pipeline have long names, and those names are non-unique in the early part of the name.
• The pipeline contains more than one of the following Data Mining Preprocessing nodes in the same branch of the flow: Imputation, Transformations, Replacement.

Under those conditions, when the nodes create variables that are assigned with the prefix _DUP, these variables might be overwritten and dropped in successor nodes.

There are no errors or warnings to indicate a problem.

You might be able to avoid the problem by using short names for variables or by making sure that variable names are unique within the first few characters.

Examples

Non-unique variable names:

• my_really_long_variable_name_1
• my_really_long_variable_name_2

Unique variable names:

• my_1 _really_long_variable_name
• my_2 _really_long_variable_name

Click the Hot Fix tab in this note for a link to instructions about accessing and applying the software update.

Operating System and Release Information

Viya on Windows: An update for this issue is available for SAS Viya 3.5. For instructions on how to access and apply software updates, see the Updating Your SAS Viya software section in the SAS Viya 3.5 for Windows Deployment Guide at

Viya on Linux: An update for this issue is available for SAS Viya 3.5. For instructions on how to access and apply software updates, see the Updating Your SAS Viya software section in the SAS Viya 3.5 for Linux Deployment Guide at