<?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>Linear-Regression on The Probability Engine</title><link>https://carlosdanieljimenez.com/tags/linear-regression/</link><description>Recent content in Linear-Regression on The Probability Engine</description><generator>Hugo -- 0.147.3</generator><language>en-us</language><lastBuildDate>Thu, 12 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://carlosdanieljimenez.com/tags/linear-regression/index.xml" rel="self" type="application/rss+xml"/><item><title>Statistical Learning: Foundations, Bias-Variance and the Art of Estimation</title><link>https://carlosdanieljimenez.com/post/2026-03-12-statistical-learning-foundations/</link><pubDate>Thu, 12 Mar 2026 00:00:00 +0000</pubDate><guid>https://carlosdanieljimenez.com/post/2026-03-12-statistical-learning-foundations/</guid><description>A rigorous walkthrough of ISLP Chapter 2 fundamentals — from the formal definition of f(X) to the bias-variance decomposition, Bayes classifiers, and KNN — with Python code, real datasets, and connections to epistemology and learning theory.</description></item></channel></rss>