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    <title>topic Modelling for High Event Rate in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350942#M5220</link>
    <description>&lt;P&gt;Hi,&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I have a dataset (30k) with event = 82% and non-event = 18%, is this still possible to use normal way to model (ie. neural/regression/etc)? So far, i dont' have a good result yet (DT, regression, scorecard, 50/50 oversampling, Neural was used). Or should i try to reverse the Event to 18% and build a model for that?&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Tue, 18 Apr 2017 17:26:03 GMT</pubDate>
    <dc:creator>okla</dc:creator>
    <dc:date>2017-04-18T17:26:03Z</dc:date>
    <item>
      <title>Modelling for High Event Rate</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350942#M5220</link>
      <description>&lt;P&gt;Hi,&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I have a dataset (30k) with event = 82% and non-event = 18%, is this still possible to use normal way to model (ie. neural/regression/etc)? So far, i dont' have a good result yet (DT, regression, scorecard, 50/50 oversampling, Neural was used). Or should i try to reverse the Event to 18% and build a model for that?&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 18 Apr 2017 17:26:03 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350942#M5220</guid>
      <dc:creator>okla</dc:creator>
      <dc:date>2017-04-18T17:26:03Z</dc:date>
    </item>
    <item>
      <title>Re: Modelling for High Event Rate</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350943#M5221</link>
      <description>&lt;P&gt;18%/82% is actually a good split in my experience.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Why do you think you don't have a good fit?&lt;/P&gt;</description>
      <pubDate>Tue, 18 Apr 2017 17:28:31 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350943#M5221</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2017-04-18T17:28:31Z</dc:date>
    </item>
    <item>
      <title>Re: Modelling for High Event Rate</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350946#M5223</link>
      <description>&lt;P&gt;All the models give me a very unstable lift chart with a lift of 1.10 at most. I am still trying out with different segments, but this will only reduce my 30k to a smaller size, which i am trying to avoid. &amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 18 Apr 2017 17:35:42 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/350946#M5223</guid>
      <dc:creator>okla</dc:creator>
      <dc:date>2017-04-18T17:35:42Z</dc:date>
    </item>
    <item>
      <title>Re: Modelling for High Event Rate</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/351160#M5224</link>
      <description>&lt;PRE&gt;
This is what Data Mining should be if you don't have a big table.
Why not try PROC LOGISTIC ,since your table is quite small .


&lt;/PRE&gt;</description>
      <pubDate>Wed, 19 Apr 2017 06:24:44 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Modelling-for-High-Event-Rate/m-p/351160#M5224</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2017-04-19T06:24:44Z</dc:date>
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