<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: GRASBOCK</title>
    <description>The latest articles on DEV Community by GRASBOCK (@grasbock).</description>
    <link>https://dev.to/grasbock</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F408086%2Fddde6e3f-2728-4594-8e2d-e8c4f5604335.jpg</url>
      <title>DEV Community: GRASBOCK</title>
      <link>https://dev.to/grasbock</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/grasbock"/>
    <language>en</language>
    <item>
      <title>What is a classifier? (visually explained)</title>
      <dc:creator>GRASBOCK</dc:creator>
      <pubDate>Tue, 19 Apr 2022 13:41:45 +0000</pubDate>
      <link>https://dev.to/grasbock/what-is-a-classifier-visually-explained-33nk</link>
      <guid>https://dev.to/grasbock/what-is-a-classifier-visually-explained-33nk</guid>
      <description>&lt;p&gt;I will start with an example:&lt;/p&gt;

&lt;p&gt;We have a dataset containing different species of cats and dogs. Each entry is a &lt;strong&gt;sample&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;For each we know the length of their tails and their ear shape (E.g. sharp tip). These are &lt;strong&gt;features&lt;/strong&gt;. &lt;/p&gt;

&lt;div class="table-wrapper-paragraph"&gt;&lt;table&gt;
&lt;thead&gt;
&lt;tr&gt;
&lt;th&gt;class&lt;/th&gt;
&lt;th&gt;tail&lt;/th&gt;
&lt;th&gt;ear&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;cat&lt;/td&gt;
&lt;td&gt;long&lt;/td&gt;
&lt;td&gt;sharp&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;dog&lt;/td&gt;
&lt;td&gt;short&lt;/td&gt;
&lt;td&gt;round&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;cat&lt;/td&gt;
&lt;td&gt;medium&lt;/td&gt;
&lt;td&gt;sharp&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;...&lt;/td&gt;
&lt;td&gt;...&lt;/td&gt;
&lt;td&gt;...&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;dog&lt;/td&gt;
&lt;td&gt;short&lt;/td&gt;
&lt;td&gt;round&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;dog&lt;/td&gt;
&lt;td&gt;medium&lt;/td&gt;
&lt;td&gt;round&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;cat&lt;/td&gt;
&lt;td&gt;long&lt;/td&gt;
&lt;td&gt;sharp&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;&lt;/div&gt;

&lt;p&gt;If we plot this dataset in a 2D graph it might look something like this &lt;strong&gt;feature space&lt;/strong&gt;:&lt;br&gt;
&lt;a href="https://res.cloudinary.com/practicaldev/image/fetch/s--omLJgWvb--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/oagc8tgy1r8x2efeudz8.png" class="article-body-image-wrapper"&gt;&lt;img src="https://res.cloudinary.com/practicaldev/image/fetch/s--omLJgWvb--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://dev-to-uploads.s3.amazonaws.com/uploads/articles/oagc8tgy1r8x2efeudz8.png" alt="cats&amp;amp;dogs feature space" width="880" height="641"&gt;&lt;/a&gt;&lt;br&gt;
Through training the &lt;strong&gt;classifier&lt;/strong&gt; determines the green decision boundary. When a new sample is given it will &lt;strong&gt;predict&lt;/strong&gt; which class it belongs to based on the region it is in.&lt;/p&gt;

&lt;p&gt;This green &lt;strong&gt;decision boundary&lt;/strong&gt; is what is usually aimed for when designing a classification algorithm. The line &lt;em&gt;(or high dimensional plane)&lt;/em&gt; might look a lot more complicated than the one in this picture and the data might be spread out across so many dimensions that we humans cannot visualize it. &lt;/p&gt;

&lt;p&gt;Machine Learning is about acquiring the samples, figuring out what features are necessary and training a classifier that makes &lt;em&gt;good&lt;/em&gt; predictions.&lt;/p&gt;

</description>
      <category>machinelearning</category>
      <category>tutorial</category>
      <category>ai</category>
    </item>
  </channel>
</rss>
