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Mike90
Quartz | Level 8

My Data Partition node is set at 40:30:30 (Train:Validate:Test)

 

My model is set up like this:

 

Data Source -> Data Partition -> Decision Tree  ->  Model Comparison

                                                -> Decision Tree  /

 

I can get a Classification Chart for the Train and Validation parts when looking at the individual Decision Tree results and in the Model Comparison results.

 

How do a get a Classification Chart for the test results?

 

Thanks

 

(In case it is somehow relevant, there are interval and nominal inputs, and the target is binary.

I used the option for Default with Inverse Prior Weights as there is a 95 / 5 distribution, and I'm

interested in improving the ability to predict the rare event, which of course cause a reduction

in the ability to predict the common event.)

 

 

 

1 ACCEPTED SOLUTION

Accepted Solutions
CraigDeVault
SAS Employee

 You will need to write your own SAS code in order to get this classification matrix.  If you add a SAS Code node after the Model Comparison node, you can write the following code to get the classification matrix:

***I am using the target variable named target.  If my target variable is bad, then I use f_bad and I_bad (assuming no decisions or prior probabilities set).

proc freq data=&em_import_test;
tables f_target*I_target;
run;

 

***I am using the target variable named target.  If my target variable is bad, then I use f_bad and I_bad (assuming decisions are set).

proc freq data=&em_import_test;
tables f_target*d_target;
run;

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2 REPLIES 2
AnujaNagpal
SAS Employee

You will see charts like ROC, score ranking overlays and other fit statistics for test data in model comparison node output.

 

CraigDeVault
SAS Employee

 You will need to write your own SAS code in order to get this classification matrix.  If you add a SAS Code node after the Model Comparison node, you can write the following code to get the classification matrix:

***I am using the target variable named target.  If my target variable is bad, then I use f_bad and I_bad (assuming no decisions or prior probabilities set).

proc freq data=&em_import_test;
tables f_target*I_target;
run;

 

***I am using the target variable named target.  If my target variable is bad, then I use f_bad and I_bad (assuming decisions are set).

proc freq data=&em_import_test;
tables f_target*d_target;
run;

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