<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayes-Rule on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/bayes-rule/</link><description>Recent content in Bayes-Rule on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Fri, 10 Jul 2026 08:20:16 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/bayes-rule/index.xml" rel="self" type="application/rss+xml"/><item><title>Email SPAM Classifier</title><link>https://abaj.ai/wiki/ml/supervised/classification/naive-bayes/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ml/supervised/classification/naive-bayes/</guid><description>&lt;p>naive bayes is the classifier you get by taking bayes&amp;rsquo; rule seriously and probability theory not seriously at all.&lt;span class="margin-note" data-note="the older literature calls it &amp;#39;idiot&amp;#39;s bayes&amp;#39;, which is unfair to a model that keeps beating its critics on small text corpora">
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it assumes every feature is independent of every other feature given the class — an assumption that is false for essentially all real data — and yet it filters spam, routes support tickets and triages documents well enough that it has survived five decades of fancier competition. this page derives it, counts why the &amp;ldquo;naive&amp;rdquo; part is the whole point, builds a spam filter from scratch, and is honest about where it breaks (its probabilities, not its decisions).&lt;/p></description></item></channel></rss>