<?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 Forest-Based regression with non-normally distributed data? in ArcGIS GeoStatistical Analyst Questions</title>
    <link>https://community.esri.com/t5/arcgis-geostatistical-analyst-questions/forest-based-regression-with-non-normally/m-p/1173980#M1773</link>
    <description>&lt;P&gt;Does anyone know how robust the Forest-Based regression is to non-normally distributed data? I've tried every data transformation possible and settled with a square root transformation which turned out to be the "least unsuccessful transformation. The subsequent model was still able to get a good R2 of 0.91, but I'm wondering how robust/sensitive the forest-based algorithm is.&lt;/P&gt;</description>
    <pubDate>Sat, 14 May 2022 03:04:57 GMT</pubDate>
    <dc:creator>JustinLee</dc:creator>
    <dc:date>2022-05-14T03:04:57Z</dc:date>
    <item>
      <title>Forest-Based regression with non-normally distributed data?</title>
      <link>https://community.esri.com/t5/arcgis-geostatistical-analyst-questions/forest-based-regression-with-non-normally/m-p/1173980#M1773</link>
      <description>&lt;P&gt;Does anyone know how robust the Forest-Based regression is to non-normally distributed data? I've tried every data transformation possible and settled with a square root transformation which turned out to be the "least unsuccessful transformation. The subsequent model was still able to get a good R2 of 0.91, but I'm wondering how robust/sensitive the forest-based algorithm is.&lt;/P&gt;</description>
      <pubDate>Sat, 14 May 2022 03:04:57 GMT</pubDate>
      <guid>https://community.esri.com/t5/arcgis-geostatistical-analyst-questions/forest-based-regression-with-non-normally/m-p/1173980#M1773</guid>
      <dc:creator>JustinLee</dc:creator>
      <dc:date>2022-05-14T03:04:57Z</dc:date>
    </item>
    <item>
      <title>Re: Forest-Based regression with non-normally distributed data?</title>
      <link>https://community.esri.com/t5/arcgis-geostatistical-analyst-questions/forest-based-regression-with-non-normally/m-p/1177633#M1774</link>
      <description>&lt;P&gt;&lt;a href="https://community.esri.com/t5/user/viewprofilepage/user-id/113929"&gt;@JustinLee&lt;/a&gt;&amp;nbsp;Great question!&amp;nbsp; Forest-based Classification and Regression does not make any normal distribution assumption about the data.&amp;nbsp; Generally speaking, outliers and extreme values will be most problematic for the model.&amp;nbsp; Ideally, you'll have a roughly even spread of values between the minimum and maximum, but there's no requirement that the distribution be bell-shaped.&lt;/P&gt;</description>
      <pubDate>Thu, 26 May 2022 17:32:37 GMT</pubDate>
      <guid>https://community.esri.com/t5/arcgis-geostatistical-analyst-questions/forest-based-regression-with-non-normally/m-p/1177633#M1774</guid>
      <dc:creator>EricKrause</dc:creator>
      <dc:date>2022-05-26T17:32:37Z</dc:date>
    </item>
  </channel>
</rss>

