<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Boosting on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/boosting/</link><description>Recent content in Boosting on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://arshadhs.github.io/tags/boosting/index.xml" rel="self" type="application/rss+xml"/><item><title>Ensemble Learning</title><link>https://arshadhs.github.io/docs/ai/machine-learning/09-ensemble-learning/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/machine-learning/09-ensemble-learning/</guid><description>&lt;h1 id="ensemble-learning">
 Ensemble Learning
 
 &lt;a class="anchor" href="#ensemble-learning">#&lt;/a>
 
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&lt;p>Ensemble Learning is a machine learning approach where we combine &lt;strong>multiple models&lt;/strong> to produce a stronger final prediction.&lt;/p>
&lt;p>Instead of depending on one model, an ensemble uses a group of models and combines their outputs.&lt;/p>
&lt;p>The main idea is simple:&lt;/p>

&lt;blockquote class='book-hint '>
 &lt;p>Many weak or moderately good models can work together to produce a better and more stable model.&lt;/p>
&lt;/blockquote>&lt;blockquote class="book-hint info">
&lt;p>&lt;strong>Key takeaway:&lt;/strong>&lt;br>
Ensemble Learning improves prediction by combining several models.&lt;/p></description></item></channel></rss>