<?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>Sklearn on wid's blog</title><link>https://wid-blog.pages.dev/tags/sklearn/</link><description>Recent content in Sklearn on wid's blog</description><generator>Hugo</generator><language>ko</language><lastBuildDate>Sun, 01 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://wid-blog.pages.dev/tags/sklearn/index.xml" rel="self" type="application/rss+xml"/><item><title>LR 기반 ML Lifecycle 도전기</title><link>https://wid-blog.pages.dev/posts/career/dable/dsp-fallback-ctr-ml-lifecycle/</link><pubDate>Sun, 01 Mar 2026 00:00:00 +0000</pubDate><guid>https://wid-blog.pages.dev/posts/career/dable/dsp-fallback-ctr-ml-lifecycle/</guid><description>AI 배경이 없는 백엔드 엔지니어로서 DSP Fallback CTR을 위한 첫 ML Lifecycle 3단 구조를 만들며 내린 기술 결정들과, 운영 끝에 배운 것들.</description></item><item><title>모델 학습 프레임워크 고르기: sklearn vs ONNX</title><link>https://wid-blog.pages.dev/posts/tech/ml/model-training-frameworks/</link><pubDate>Sun, 15 Feb 2026 00:00:00 +0000</pubDate><guid>https://wid-blog.pages.dev/posts/tech/ml/model-training-frameworks/</guid><description>sklearn과 ONNX는 같은 레이어의 경쟁자가 아니다. 두 도구의 자리를 분리해서 보면 &amp;lsquo;ONNX 레이어가 필요한가&amp;rsquo;라는 질문이 자연스럽게 남는다.</description></item></channel></rss>