Demo: Scoring a Logistic Regression Model (Self-Paced)

In this demonstration, you score a new data set using the score code from the logistic regression model that you created earlier. This enables you to add predictions from a trained model to a score data set.

Reminder: If you restarted your SAS session, or it has timed out due to inactivity, you must re-create the course libraries, LOCALLIB and VST. To do this, run the AssignLibrary flow.

  1. In SAS Viya, launch SAS Studio by clicking the Applications menu button (the three-by-three grid in the upper left corner), expand ANALYTICS LIFE CYCLE, and click Develop Code and Flows.
  2. In the navigation panel on the far left, click the Explorer button (second from top). Expand Files >Home > workshop > VST. Double-click AssignLibrary.flw to open it.
  3. Click the Run button on the flow toolbar.

Demo Steps

  1. Open the previously created flow Categorical Data Analysis.flw from the VST folder. We will add a program to this flow.
    2. Still in the VST folder, double click on Scoring Logistic.sas and view the code.
  2. The SAS Program contains the following code. It uses the score code from the logistic regression model to score the SCORE_PVA data set.
  3. The predictions are saved to the PVAScored data set:


    4. Open code or syntax in a separate window. 

    data VST.PVAScored;
  set VST.SCORE_PVA;
  %include "/home/student/workshop/VST/logistic_score.sas";
run;

proc contents data=VST.PVAScored;
run;

  4. Click the + Code to Flow button and add the program to the previously saved Categorical Data Analysis flow. Navigate to the flow tab of Categorical Data Anlysis.flw.
  5. Right-click on the Scoring Logistic node and select Run node. Save the Categorical Data Analysis flow.
  6. Examine the Results as well as the Output Data tab. Note the variables P_response and I_response in both the Results and the Output Data tabs

A screenshot of the Alphabetic List of Variables and Attributes.


Artial view of the output table (PVASCORED) is shown below:

A screenshot of the output table.

The P_Response column shows the predicted probability of the target Response=Yes. The I_Response column is an indicator variable that takes the value of Yes when the value of P_Response is greater than or equal to 0.5 and No otherwise.

Note: Your output might show a different listing of scored data.