<?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>Model-Selection on The Standard Error</title><link>https://jongha-jeon-dev.github.io/standarderror/tags/model-selection/</link><description>Recent content in Model-Selection on The Standard Error</description><generator>Hugo -- 0.123.7</generator><language>en-gb</language><lastBuildDate>Wed, 14 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jongha-jeon-dev.github.io/standarderror/tags/model-selection/index.xml" rel="self" type="application/rss+xml"/><item><title>ML 20: Same Accuracy, Different Answers</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-20-rashomon/</link><pubDate>Mon, 12 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-20-rashomon/</guid><description>Two models with the same test accuracy are usually treated as interchangeable. On a hard task, networks that differ only in their seed give different answers on a fifth of the rows.</description></item><item><title>ML 57: The BIC Counts Gaussians, Not Clusters</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-57-bic/</link><pubDate>Wed, 14 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-57-bic/</guid><description>Given one cluster shaped like a square, the BIC asked for more Gaussian components the more data it saw, 18 at 20,000 points. It was answering a question about shape and sample size, not about clusters.</description></item><item><title>ML 60: The Most Stable k Is Not the Number of Clusters</title><link>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-60-stability/</link><pubDate>Wed, 14 Oct 2026 00:00:00 +0000</pubDate><guid>https://jongha-jeon-dev.github.io/standarderror/lectures/ml-60-stability/</guid><description>Resampling found k-means&amp;#39; two-way split of a single Gaussian as reproducible as three real clusters. Stability measures whether the method has one best answer, and cannot say that there are no clusters.</description></item></channel></rss>