<?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>광고 on wid's blog</title><link>https://wid-blog.pages.dev/tags/%EA%B4%91%EA%B3%A0/</link><description>Recent content in 광고 on wid's blog</description><generator>Hugo</generator><language>ko</language><lastBuildDate>Sat, 07 Feb 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://wid-blog.pages.dev/tags/%EA%B4%91%EA%B3%A0/index.xml" rel="self" type="application/rss+xml"/><item><title>Logistic Regression 다시 보기</title><link>https://wid-blog.pages.dev/posts/tech/ml/logistic-regression/</link><pubDate>Sat, 07 Feb 2026 00:00:00 +0000</pubDate><guid>https://wid-blog.pages.dev/posts/tech/ml/logistic-regression/</guid><description>CTR 예측의 baseline으로서 Logistic Regression의 구조와 특성을 정리한다. 오래된 모델이 여전히 그 자리에 있는 이유.</description></item></channel></rss>