Monitoring Performance of a Model without Score Code

If you want to monitor the performance of a model for which you no longer have the score code, you can import a model without SAS score code. If the performance data set contains the predicted values, the score.sas file can be empty.

To monitor the performance of a model without score code:

Prepare the following model files:
- XML file that defines the model input variables (inputvar.xml)
- XML file that defines the model output variables (outputvar.xml)
- XML file that defines the model target variables (targetvar.xml)
- empty SAS score code file (score.sas)
Create a project that has a model function type of Classification or Prediction, and create a version. You can skip this step if you have already created a project and version.
If it is a project that has a model function property value of Classification, verify that the following project properties are set:
- Training Target Variable (for example, bad)
- Target Event Value (for example, 1)
- Class Target Level as Binary
- Output Event Probability Variable (for example, score)
If it is a project that has a model function property value of Prediction, verify that the following project properties are set:
- Training Target Variable (for example, lgd)
- Class Target Level as Interval
- Output Prediction Variable (for example, p_lgd)
In the Project Tree, navigate to the project’s version.

MMRootorganizational folderproject folderversion folder
Right-click Models and select Import fromLocal Files.

Note: If the model already exists, you can right-click the model name and select Partial Import to import an empty score.sas file, and then skip to step 11. For more information, see Import Partial Models.
Navigate to the folder on your computer that contains the component files for your model.
Select a classification or prediction template from the Choose a model template list.
Enter a text value in the model Name field.
Complete the template fields. Drag the files from the left of the window to the corresponding file property on the right. The following files are required:
- inputvar.xml
- outputvar.xml
- targetvar.xml
- score.sas
Note: The filenames that you created for the model do not have to match the template filenames. However, the file contents must meet the file property requirements. For more information, see Model Template Component Files or Model Template Properties.
Click OK. After SAS Model Manager processes the model import request, the new model appears in the Models folder of your project's version.
Select the model in the Project Tree, and set the model-specific properties. The value for the Score Code Type property must be set to DATA step.
Right-click the model, and select Set Model Output Mapping in order to set the output variable mappings for the model. Click the list in the Models Variables column and select the model output variable. Click OK.
Right-click the champion model and select Set as Champion. For more information, see Ensure That the Champion Model Is Set or That the Challenger Model Is Flagged.
Before defining a performance task, verify that the performance data set is registered in SAS Management Console or that a libref has been defined for the performance data set library using the Edit Start-up Code window. Make sure that the data set contains the following variables:
- model input variables
  
  Note: You must have the variable columns in the table, but the values can be missing.
- target variable
- prediction variables
- variables for characteristic analysis
Define a performance task using the performance data set that contains the predicted values. Also, be sure to clear the Run model score code option for the Data Processing Method section of the Define Performance Task wizard. For more information, see Run the Define Performance Task Wizard.