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    <title>topic Re: Testing Confusion matrix in Enterprise Miner in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Testing-Confusion-matrix-in-Enterprise-Miner/m-p/386090#M5700</link>
    <description>&lt;P&gt;SAS Enterprise Miner generates a ROC curve for the Train, Validate, and Test data set in the Model Comparison node when modeling a binary target. &amp;nbsp;It also generates a misclassification chart for the Train &amp;amp; Validate data sets but it does not generate a misclassification chart for the Test data set. &amp;nbsp;In the design of SAS Enterprise Miner, Test data sets are intended for a final unbiased evaluation of model performance so they are not used by default when a Validate data set is present. &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Please note that SAS Enterprise Miner always generates&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp;F_&amp;lt;target variable name&amp;gt; : the target variable value &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp;I _&amp;lt;target variable name&amp;gt; : the predicted target value (based on highest probability)&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;but it can also generates a D _&amp;lt;target variable name&amp;gt; &amp;nbsp;which contains the 'decision' outcome based on the decision weights and priors entered in the target profile when one is present. &amp;nbsp; &amp;nbsp; For example, if the target variable is named 'BAD', SAS Enterprise Miner would create the variables F_BAD, I_BAD, and D_BAD. &amp;nbsp; &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If a Test data set is available, you can add a SAS Code node after any modeling node and enter the following code in the Training code section. &amp;nbsp;This example assumes the target variable is named BAD. &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;/*** BEGIN SAS CODE ***/&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;proc freq data=&amp;amp;em_import_test; &lt;BR /&gt; tables F_BAD*I_BAD;&lt;/P&gt;
&lt;P&gt;tables F_BAD*I_BAD; &amp;nbsp;*only available if Decision profile has been created;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;/*** END SAS CODE ***/&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;The code above will generate both misclassification charts if the target profile is available.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Hope this helps!&lt;/P&gt;
&lt;P&gt;Doug&lt;/P&gt;</description>
    <pubDate>Mon, 07 Aug 2017 17:26:29 GMT</pubDate>
    <dc:creator>DougWielenga</dc:creator>
    <dc:date>2017-08-07T17:26:29Z</dc:date>
    <item>
      <title>Testing Confusion matrix in Enterprise Miner</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Testing-Confusion-matrix-in-Enterprise-Miner/m-p/364432#M5443</link>
      <description>&lt;P&gt;Hi,&amp;nbsp;&lt;/P&gt;&lt;P&gt;I wonder if someone can help me, I am using model comparison node and getting following kind of classification table for validation and training datasets.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;False&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; True&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; False&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; True&lt;/P&gt;&lt;P&gt;Negative&amp;nbsp;&amp;nbsp;&amp;nbsp; Negative&amp;nbsp;&amp;nbsp;&amp;nbsp; Positive&amp;nbsp;&amp;nbsp;&amp;nbsp; Positive&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&amp;nbsp; 364&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 4518&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 253&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; 825&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Can anyone please tell me how do we get these for testing dataset?&amp;nbsp;&lt;/P&gt;&lt;P&gt;In general do we need to find out for test datasets?&lt;/P&gt;&lt;P&gt;When we write these readings do we write aggregated values or for individual values?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Regards&lt;/P&gt;</description>
      <pubDate>Tue, 06 Jun 2017 01:16:55 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Testing-Confusion-matrix-in-Enterprise-Miner/m-p/364432#M5443</guid>
      <dc:creator>geniusgenie</dc:creator>
      <dc:date>2017-06-06T01:16:55Z</dc:date>
    </item>
    <item>
      <title>Re: Testing Confusion matrix in Enterprise Miner</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Testing-Confusion-matrix-in-Enterprise-Miner/m-p/386090#M5700</link>
      <description>&lt;P&gt;SAS Enterprise Miner generates a ROC curve for the Train, Validate, and Test data set in the Model Comparison node when modeling a binary target. &amp;nbsp;It also generates a misclassification chart for the Train &amp;amp; Validate data sets but it does not generate a misclassification chart for the Test data set. &amp;nbsp;In the design of SAS Enterprise Miner, Test data sets are intended for a final unbiased evaluation of model performance so they are not used by default when a Validate data set is present. &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Please note that SAS Enterprise Miner always generates&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp;F_&amp;lt;target variable name&amp;gt; : the target variable value &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp; &amp;nbsp; &amp;nbsp;I _&amp;lt;target variable name&amp;gt; : the predicted target value (based on highest probability)&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;but it can also generates a D _&amp;lt;target variable name&amp;gt; &amp;nbsp;which contains the 'decision' outcome based on the decision weights and priors entered in the target profile when one is present. &amp;nbsp; &amp;nbsp; For example, if the target variable is named 'BAD', SAS Enterprise Miner would create the variables F_BAD, I_BAD, and D_BAD. &amp;nbsp; &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If a Test data set is available, you can add a SAS Code node after any modeling node and enter the following code in the Training code section. &amp;nbsp;This example assumes the target variable is named BAD. &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;/*** BEGIN SAS CODE ***/&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;proc freq data=&amp;amp;em_import_test; &lt;BR /&gt; tables F_BAD*I_BAD;&lt;/P&gt;
&lt;P&gt;tables F_BAD*I_BAD; &amp;nbsp;*only available if Decision profile has been created;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;/*** END SAS CODE ***/&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;The code above will generate both misclassification charts if the target profile is available.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Hope this helps!&lt;/P&gt;
&lt;P&gt;Doug&lt;/P&gt;</description>
      <pubDate>Mon, 07 Aug 2017 17:26:29 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Testing-Confusion-matrix-in-Enterprise-Miner/m-p/386090#M5700</guid>
      <dc:creator>DougWielenga</dc:creator>
      <dc:date>2017-08-07T17:26:29Z</dc:date>
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