<?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>Divergence Metrics on Sayan Chatterjee</title><link>https://www.sayanchatterjee.org/tags/divergence-metrics/</link><description>Recent content in Divergence Metrics on Sayan Chatterjee</description><image><title>Sayan Chatterjee</title><url>https://www.sayanchatterjee.org/images/sayan-chatterjee-ai-architect.jpg</url><link>https://www.sayanchatterjee.org/images/sayan-chatterjee-ai-architect.jpg</link></image><generator>Hugo -- 0.146.0</generator><language>en-us</language><lastBuildDate>Tue, 07 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.sayanchatterjee.org/tags/divergence-metrics/index.xml" rel="self" type="application/rss+xml"/><item><title>Beyond Accuracy: Measuring Divergence Between Actual and Predicted Distributions in Machine Learning</title><link>https://www.sayanchatterjee.org/blogs/divergence-between-actual-and-predicted-distributions/</link><pubDate>Tue, 07 Apr 2026 00:00:00 +0000</pubDate><guid>https://www.sayanchatterjee.org/blogs/divergence-between-actual-and-predicted-distributions/</guid><description>ML evaluation goes beyond prediction error. Measuring distribution alignment with the right divergence metric improves reliability, robustness, and trust.</description></item></channel></rss>