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    <title>topic Re: GLM not predicting lower values in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/282832#M4212</link>
    <description>Sometimes you are dealing with two different sets of underlying drivers, so something that might work is to see if you can identify those who spend less than $50 using a binary regression model first. If you can predict those (meaning you are getting a strong model), simply run the binary model first, and then run a separate GLM for each. I'll bet you can see which variables are different by comparing the GLM results.</description>
    <pubDate>Fri, 08 Jul 2016 00:08:53 GMT</pubDate>
    <dc:creator>JBerry</dc:creator>
    <dc:date>2016-07-08T00:08:53Z</dc:date>
    <item>
      <title>GLM not predicting lower values</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/282451#M4197</link>
      <description>&lt;P&gt;Hi,&lt;BR /&gt;I am trying to predict an interval target (customer spend in $) using GLM in SAS EM 13.2. I have 80K observations and 300 variables.&lt;BR /&gt;The problem that I have with the final model is that the minimum prediction for the target is around $50 whereas 11K of customers have spent less than $50 in the training dataset and 1% of customers have spent less than $1.&lt;BR /&gt;&lt;BR /&gt;Any thoughts on why is this happening or on how to fix it?&lt;BR /&gt;The probability distribution that I have used in the GLM model is Gamma with a Log link function. I have also tried other prob. distributions as well as link functions like Tweedie or invert gaussian but Gamma with a log link function produced the smallest ASE. The distribution of Target variable is highly skewed on the right as there are plenty of customers spending lower amounts and only a few spending more than $1000.&lt;BR /&gt;&lt;BR /&gt;Any thoughts are highly appreciated,&lt;BR /&gt;Thanks,&lt;BR /&gt;Sam&lt;/P&gt;</description>
      <pubDate>Wed, 06 Jul 2016 15:28:59 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/282451#M4197</guid>
      <dc:creator>Sam_zirak</dc:creator>
      <dc:date>2016-07-06T15:28:59Z</dc:date>
    </item>
    <item>
      <title>Re: GLM not predicting lower values</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/282832#M4212</link>
      <description>Sometimes you are dealing with two different sets of underlying drivers, so something that might work is to see if you can identify those who spend less than $50 using a binary regression model first. If you can predict those (meaning you are getting a strong model), simply run the binary model first, and then run a separate GLM for each. I'll bet you can see which variables are different by comparing the GLM results.</description>
      <pubDate>Fri, 08 Jul 2016 00:08:53 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/282832#M4212</guid>
      <dc:creator>JBerry</dc:creator>
      <dc:date>2016-07-08T00:08:53Z</dc:date>
    </item>
    <item>
      <title>Re: GLM not predicting lower values</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/283551#M4215</link>
      <description>&lt;P&gt;Thank you JBerry,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I really liked your idea but I am still confused that why GLM is&amp;nbsp;unable to predict those small spends even without segmenting the whole population.&lt;/P&gt;&lt;P&gt;I have already segmented the population based on the credit limit of customers: CL le $500 and CL g $500.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks,&lt;/P&gt;&lt;P&gt;Sam&lt;/P&gt;</description>
      <pubDate>Mon, 11 Jul 2016 17:26:14 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/GLM-not-predicting-lower-values/m-p/283551#M4215</guid>
      <dc:creator>Sam_zirak</dc:creator>
      <dc:date>2016-07-11T17:26:14Z</dc:date>
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