https://communities.sas.com/t5/SAS-Data-Science/Confusion-Matrix-for-a-Nominal-Target-Variable-in-Enterprise/m-p/392654#M5949 <P>In the Results of the Model Comparison node, go to View&gt;Assessment&gt;Classification Chart and this displays the information that I think you need, and you can see the numbers by then selecting View&gt;Table. &nbsp;The information is displayed in a slightly different manner than below, but gives you the % correct and incorrect for each level of the target for each model.</P> <P>Hope that helps!</P> <P>&nbsp;</P> Fri, 01 Sep 2017 19:55:22 GMT WendyCzika 2017-09-01T19:55:22Z https://communities.sas.com/t5/SAS-Data-Science/Confusion-Matrix-for-a-Nominal-Target-Variable-in-Enterprise/m-p/392603#M5948 <P>I have a nominal target variable with 6 levels that I am builidng a classification model for.&nbsp; In the out put of the model selection node the Event Classification Table (Confusion Matrix) looks like it is showing the results for just one of the levels of the target variable (see below for output)</P><P>&nbsp;</P><P>Why is that and how do I get it to show the results for all levels of the target variable?? I am not a very good base sas programer by the way. &nbsp;Also, it looks like performance measures are being calculated off the results for just this one level, is that true?</P><P>&nbsp;</P><P>Event Classification Table<BR />Model Selection based on Valid: Roc Index (_VAUR_)<BR />&nbsp;<BR />&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Target&nbsp;&nbsp; False&nbsp;&nbsp;&nbsp; True&nbsp;&nbsp;&nbsp;&nbsp; False&nbsp;&nbsp;&nbsp; True<BR />Model Node&nbsp; Model Description Role&nbsp;&nbsp;&nbsp;&nbsp; Target&nbsp; Label&nbsp; Negative Negative Positive Positive<BR />&nbsp;<BR />TextRule&nbsp;&nbsp;&nbsp; Text Rule Builder TRAIN&nbsp;&nbsp;&nbsp; Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 650&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7<BR />TextRule&nbsp;&nbsp;&nbsp; Text Rule Builder VALIDATE Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 442&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7<BR />Tree&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Decision Tree&nbsp;&nbsp;&nbsp;&nbsp; TRAIN&nbsp;&nbsp;&nbsp; Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 647&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9<BR />Tree&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Decision Tree&nbsp;&nbsp;&nbsp;&nbsp; VALIDATE Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 435&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 6<BR />Boost&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Gradient Boosting TRAIN&nbsp;&nbsp;&nbsp; Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 652&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7<BR />Boost&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Gradient Boosting VALIDATE Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 442&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3<BR />AutoNeural2 AutoNeural (2)&nbsp;&nbsp;&nbsp; TRAIN&nbsp;&nbsp;&nbsp; Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 651&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9<BR />AutoNeural2 AutoNeural (2)&nbsp;&nbsp;&nbsp; VALIDATE Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 441&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7<BR />Ensmbl&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Ensemble&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; TRAIN&nbsp;&nbsp;&nbsp; Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 652&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; .&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 6<BR />Ensmbl&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Ensemble&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VALIDATE Product&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 440&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5</P> Fri, 01 Sep 2017 17:24:24 GMT https://communities.sas.com/t5/SAS-Data-Science/Confusion-Matrix-for-a-Nominal-Target-Variable-in-Enterprise/m-p/392603#M5948 josh_winters 2017-09-01T17:24:24Z https://communities.sas.com/t5/SAS-Data-Science/Confusion-Matrix-for-a-Nominal-Target-Variable-in-Enterprise/m-p/392654#M5949 <P>In the Results of the Model Comparison node, go to View&gt;Assessment&gt;Classification Chart and this displays the information that I think you need, and you can see the numbers by then selecting View&gt;Table. &nbsp;The information is displayed in a slightly different manner than below, but gives you the % correct and incorrect for each level of the target for each model.</P> <P>Hope that helps!</P> <P>&nbsp;</P> Fri, 01 Sep 2017 19:55:22 GMT https://communities.sas.com/t5/SAS-Data-Science/Confusion-Matrix-for-a-Nominal-Target-Variable-in-Enterprise/m-p/392654#M5949 WendyCzika 2017-09-01T19:55:22Z