<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Glm on blog.pierrehenry.be</title><link>https://blog.pierrehenry.be/tags/glm/</link><description>Recent content in Glm on blog.pierrehenry.be</description><generator>Hugo</generator><language>en-US</language><copyright>Copyright © 2026, Pierre-Henry Soria.</copyright><lastBuildDate>Tue, 30 Dec 2025 16:48:07 +0000</lastBuildDate><atom:link href="https://blog.pierrehenry.be/tags/glm/index.xml" rel="self" type="application/rss+xml"/><item><title>How to Build Your First GLM in Python Without Getting Lost</title><link>https://blog.pierrehenry.be/blog/how-to-build-your-first-glm-in-python-without-getting-lost/</link><pubDate>Tue, 30 Dec 2025 16:48:07 +0000</pubDate><guid>https://blog.pierrehenry.be/blog/how-to-build-your-first-glm-in-python-without-getting-lost/</guid><description>Alright, let’s dive right in. I want to walk you through a little data science learning project I’ve been working on—a GLM, or generalized linear model. If you’re just getting started with regressi&amp;hellip;</description></item><item><title>How to Build Smarter AI Predictions Without Complex Code</title><link>https://blog.pierrehenry.be/blog/how-to-build-smarter-ai-predictions-without-complex-code/</link><pubDate>Tue, 30 Dec 2025 11:16:43 +0000</pubDate><guid>https://blog.pierrehenry.be/blog/how-to-build-smarter-ai-predictions-without-complex-code/</guid><description>Alright, let’s get right into it. Today, I’m going to walk you through a project I’ve been working on that uses generalized linear models—specifically, logistic regression. Most of my experiments h&amp;hellip;</description></item></channel></rss>