<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes. in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/676119#M8380</link>
    <description>If I was right, left click the 'logistic' node ,and at left panel you could find an option PEVENT</description>
    <pubDate>Wed, 12 Aug 2020 11:23:48 GMT</pubDate>
    <dc:creator>Ksharp</dc:creator>
    <dc:date>2020-08-12T11:23:48Z</dc:date>
    <item>
      <title>Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675537#M8371</link>
      <description>&lt;P&gt;I am trying to do logistic regression, decision tree, KNN &amp;amp; neural network on a dataset where I have 9800 rows, the target is binary and 98% 0. I have 1000 interval predictors, all the variables have many 0s and not normal distributions. How should I approach to handle the imbalanced data in SAS Miner for each of the models? Can somebody pls help?&lt;/P&gt;</description>
      <pubDate>Mon, 10 Aug 2020 06:14:36 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675537#M8371</guid>
      <dc:creator>akmsharif</dc:creator>
      <dc:date>2020-08-10T06:14:36Z</dc:date>
    </item>
    <item>
      <title>Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675556#M8372</link>
      <description>Oversample , make good:bad  about  3:1 or 4:1 .</description>
      <pubDate>Mon, 10 Aug 2020 11:24:05 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675556#M8372</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2020-08-10T11:24:05Z</dc:date>
    </item>
    <item>
      <title>Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675758#M8376</link>
      <description>Can you please let me know a bit detail?</description>
      <pubDate>Mon, 10 Aug 2020 21:12:33 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675758#M8376</guid>
      <dc:creator>akmsharif</dc:creator>
      <dc:date>2020-08-10T21:12:33Z</dc:date>
    </item>
    <item>
      <title>Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675814#M8377</link>
      <description>&lt;P&gt;Could you please take a look at the question of mine that I posted in my page&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/18408"&gt;@Ksharp&lt;/a&gt;&amp;nbsp;? Thank you very much.&lt;/P&gt;</description>
      <pubDate>Tue, 11 Aug 2020 03:56:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675814#M8377</guid>
      <dc:creator>janex</dc:creator>
      <dc:date>2020-08-11T03:56:18Z</dc:date>
    </item>
    <item>
      <title>Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675886#M8378</link>
      <description>&lt;P&gt;Due to every small event probability , any model would not be trusted.&lt;/P&gt;
&lt;P&gt;Oversample stands for enhancing event prob, if you have 1000 obs only 10 obs is 1,you need randomly sample 30 or 40 from the remain 990 obs which is 0 to form a train data to model . a.k.a 1:0 is about 1:3 or 1:4 .&lt;/P&gt;
&lt;P&gt;if you are using PROC LOGISTIC ,don't forget to use PEVENT=0.01 to adjust predicted prob .&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;And&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/13684"&gt;@Rick_SAS&lt;/a&gt;&amp;nbsp; maybe have good ideas.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Tue, 11 Aug 2020 11:54:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/675886#M8378</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2020-08-11T11:54:18Z</dc:date>
    </item>
    <item>
      <title>Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/676065#M8379</link>
      <description>&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/18408"&gt;@Ksharp&lt;/a&gt;, &lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/13684"&gt;@Rick_SAS&lt;/a&gt;:&lt;BR /&gt;I am using SAS MIner.. How should I apply the PEVENT function there?&lt;BR /&gt;Thanks.&lt;BR /&gt;</description>
      <pubDate>Wed, 12 Aug 2020 04:06:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/676065#M8379</guid>
      <dc:creator>akmsharif</dc:creator>
      <dc:date>2020-08-12T04:06:40Z</dc:date>
    </item>
    <item>
      <title>Re: Solution for imbalanced data, categorical target, 98% weights on 0 outcomes.</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/676119#M8380</link>
      <description>If I was right, left click the 'logistic' node ,and at left panel you could find an option PEVENT</description>
      <pubDate>Wed, 12 Aug 2020 11:23:48 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Solution-for-imbalanced-data-categorical-target-98-weights-on-0/m-p/676119#M8380</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2020-08-12T11:23:48Z</dc:date>
    </item>
  </channel>
</rss>

