<?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>Ensembles on The Standard Error</title><link>https://jongha-jeon-dev.github.io/standarderror/tags/ensembles/</link><description>Recent content in Ensembles on The Standard Error</description><generator>Hugo -- 0.123.7</generator><language>en-gb</language><lastBuildDate>Tue, 13 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jongha-jeon-dev.github.io/standarderror/tags/ensembles/index.xml" rel="self" type="application/rss+xml"/><item><title>ML 41: An Average of Models Beats the Average Model, Not the Best One</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-41-averaging/</link><pubDate>Tue, 13 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-41-averaging/</guid><description>Averaging carries a guarantee, and it is weaker than the one usually quoted. When one member is certain about everything, averaging probabilities hands it every decision.</description></item><item><title>ML 42: Stacking Learns to Pick the Best Model, Given Honest Predictions</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-42-stacking/</link><pubDate>Tue, 13 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-42-stacking/</guid><description>A meta-model trusts whichever base model looks best on the predictions it is fitted to. On in-sample predictions that is the best memoriser; on honest ones it is usually just the best model.</description></item></channel></rss>