<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sales Prediction on C.CUI's Log</title><link>https://cuicaihao.github.io/tags/sales-prediction/</link><description>Recent content in Sales Prediction on C.CUI's Log</description><generator>Hugo</generator><language>en-AU</language><lastBuildDate>Sat, 17 Oct 2026 07:00:00 +1100</lastBuildDate><atom:link href="https://cuicaihao.github.io/tags/sales-prediction/index.xml" rel="self" type="application/rss+xml"/><item><title>From a Single Tree to a Forest: Mathematical Principles, Algorithmic Evolution, and Engineering Practices of Tree Models</title><link>https://cuicaihao.github.io/posts/2026-10-17-from-a-single-tree-to-a-forest-mathematical-principles-algorithmic-evolution-and-engineering-practices-of-tree-models/</link><pubDate>Sat, 17 Oct 2026 07:00:00 +1100</pubDate><guid>https://cuicaihao.github.io/posts/2026-10-17-from-a-single-tree-to-a-forest-mathematical-principles-algorithmic-evolution-and-engineering-practices-of-tree-models/</guid><description>Starting with a single decision tree, this post explains CART, random forests, and GBDT, then compares how XGBoost, LightGBM, and CatBoost train, handle categorical features, and support evaluation and selection under real-world data problems.</description></item></channel></rss>