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    <title>topic Actual vs Predicted , help please. Thank You in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Actual-vs-Predicted-help-please-Thank-You/m-p/134602#M1223</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;BR /&gt;&lt;SPAN style="color: #000080;"&gt;Hi All,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;I have built a logistic regression model on the training dataset, then score my validation dataset. I have calculated the Mean Absolute Percentage Error&amp;nbsp; between Actual and Predicted (Table below) Is there any rule to say if my MAPE is &amp;gt; 10% for example, the model doesn't generalize well, so need to improve the model or build a new one? I would like to set up a rule. Your help would be much appreciated,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;Many Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;TABLE border="0" cellpadding="0" cellspacing="0" width="466"&gt;&lt;TBODY&gt;&lt;TR&gt;&lt;TD class="xl65" height="37" width="95"&gt;Decile Group&lt;/TD&gt;&lt;TD class="xl67" style="border-left: medium none;" width="75"&gt;Actual&lt;/TD&gt;&lt;TD class="xl67" style="border-left: medium none;" width="70"&gt;Predicted&lt;/TD&gt;&lt;TD class="xl67" style="border-left: medium none;" width="226"&gt;Absolute MAPE&lt;BR /&gt;(Mean Absolute Percentage Error)&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;10&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;588&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;601&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;2%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;9&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;494&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;497&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;1%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;8&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;472&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;442&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;6%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;7&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;402&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;403&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;0%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;6&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;392&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;374&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;5%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;5&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;327&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;347&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;6%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;4&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;303&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;320&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;5%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;3&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;316&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;297&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;6%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;2&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;267&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;271&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;2%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;1&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;232&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;242&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;4%&lt;/TD&gt;&lt;/TR&gt;&lt;/TBODY&gt;&lt;/TABLE&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Wed, 08 Jan 2014 16:26:11 GMT</pubDate>
    <dc:creator>Kanyange</dc:creator>
    <dc:date>2014-01-08T16:26:11Z</dc:date>
    <item>
      <title>Actual vs Predicted , help please. Thank You</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Actual-vs-Predicted-help-please-Thank-You/m-p/134602#M1223</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;&lt;BR /&gt;&lt;SPAN style="color: #000080;"&gt;Hi All,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;I have built a logistic regression model on the training dataset, then score my validation dataset. I have calculated the Mean Absolute Percentage Error&amp;nbsp; between Actual and Predicted (Table below) Is there any rule to say if my MAPE is &amp;gt; 10% for example, the model doesn't generalize well, so need to improve the model or build a new one? I would like to set up a rule. Your help would be much appreciated,&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN style="color: #000080;"&gt;Many Thanks&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;TABLE border="0" cellpadding="0" cellspacing="0" width="466"&gt;&lt;TBODY&gt;&lt;TR&gt;&lt;TD class="xl65" height="37" width="95"&gt;Decile Group&lt;/TD&gt;&lt;TD class="xl67" style="border-left: medium none;" width="75"&gt;Actual&lt;/TD&gt;&lt;TD class="xl67" style="border-left: medium none;" width="70"&gt;Predicted&lt;/TD&gt;&lt;TD class="xl67" style="border-left: medium none;" width="226"&gt;Absolute MAPE&lt;BR /&gt;(Mean Absolute Percentage Error)&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;10&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;588&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;601&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;2%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;9&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;494&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;497&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;1%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;8&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;472&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;442&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;6%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;7&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;402&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;403&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;0%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;6&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;392&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;374&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;5%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;5&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;327&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;347&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;6%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;4&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;303&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;320&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;5%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;3&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;316&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;297&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;6%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;2&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;267&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;271&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;2%&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD class="xl64" height="20" style="border-top: medium none;"&gt;1&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;232&lt;/TD&gt;&lt;TD class="xl66" style="border-left: medium none; border-top: medium none;"&gt;242&lt;/TD&gt;&lt;TD class="xl68" style="border-left: medium none; border-top: medium none;"&gt;4%&lt;/TD&gt;&lt;/TR&gt;&lt;/TBODY&gt;&lt;/TABLE&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 08 Jan 2014 16:26:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Actual-vs-Predicted-help-please-Thank-You/m-p/134602#M1223</guid>
      <dc:creator>Kanyange</dc:creator>
      <dc:date>2014-01-08T16:26:11Z</dc:date>
    </item>
    <item>
      <title>Re: Actual vs Predicted , help please. Thank You</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Actual-vs-Predicted-help-please-Thank-You/m-p/134603#M1224</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Any rule is going to depend on the relative cost of making an error.&amp;nbsp; In some fields, an error &amp;gt;10% may be a good cutpoint, but in others, it may need to be substantially less.&amp;nbsp; Also, percentages are notorious for being scale dependent.&amp;nbsp; In your example, a deviation of 13 with a denominator of 588, leads to a percentage error of 2%.&amp;nbsp; However, suppose your model predicted a deviation of only 1.3 (much better), but the denominator was only 5.88.&amp;nbsp; Now the percentage error is 22%, even though the absolute prediction is a magnitude more precise.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;That probably doesn't answer your question of HOW to set a cutpoint in EM, but it is something to consider when the question is put forward.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 08 Jan 2014 17:45:20 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Actual-vs-Predicted-help-please-Thank-You/m-p/134603#M1224</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2014-01-08T17:45:20Z</dc:date>
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