<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>Model-Training on Compile My Mind</title>
		<link>https://www.compilemymind.com/tags/model-training/</link>
		<description>Recent content in Model-Training on Compile My Mind</description>
		<generator>Hugo</generator>
		<language>en</language>
		
		
		
		
			<lastBuildDate>Mon, 24 Aug 2026 11:37:14 +0300</lastBuildDate>
		
			<atom:link href="https://www.compilemymind.com/tags/model-training/index.xml" rel="self" type="application/rss+xml" />
			<item>
				<title>What Is Machine Learning and How Does It Work?</title>
				<link>https://www.compilemymind.com/posts/what-is-machine-learning/</link>
				<pubDate>Mon, 24 Aug 2026 11:37:14 +0300</pubDate>
				<guid>https://www.compilemymind.com/posts/what-is-machine-learning/</guid>
				<description>&lt;p&gt;A spam filter reports 99% accuracy. That sounds excellent—until someone notices that only 1% of the messages in its test data were spam.&lt;/p&gt;&#xA;&lt;p&gt;The model could label every message &lt;em&gt;not spam&lt;/em&gt;, catch nothing dangerous, and still earn that impressive score.&lt;/p&gt;&#xA;&lt;p&gt;This is the tension at the heart of machine learning. Training a model is the visible technical step, but useful machine learning depends on everything around it: choosing the right problem, collecting representative examples, measuring the mistakes that matter, and noticing when the real world changes.&lt;/p&gt;</description>
			</item>
	</channel>
</rss>
