<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Machine-Learning on CS Theorems</title><link>https://cs.lozic.me/areas/machine-learning/</link><description>Recent content in Machine-Learning on CS Theorems</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 16 Apr 2027 12:00:00 +0100</lastBuildDate><atom:link href="https://cs.lozic.me/areas/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Minimum Description Length and Occam's Razor</title><link>https://cs.lozic.me/posts/t025-minimum-description-length-and-occams-razor/</link><pubDate>Fri, 16 Apr 2027 12:00:00 +0100</pubDate><guid>https://cs.lozic.me/posts/t025-minimum-description-length-and-occams-razor/</guid><description>&lt;h2 id="symptom"&gt;Symptom&lt;/h2&gt;
&lt;p&gt;You fit a model. It scores 94% on training data and 71% on held-out data.&lt;/p&gt;
&lt;p&gt;So you simplify: fewer parameters, more regularization. Training drops to 88%,
held-out rises to 84%. You simplify further and both drop. Somewhere in there
was an optimum, and you found it by trial and error, with a validation set and
patience.&lt;/p&gt;</description></item></channel></rss>