<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Gradient Boosting - Tag - Jorgen Bergstrom</title><link>https://bergstrom.org/tags/gradient-boosting/</link><description>Gradient Boosting - Tag - Jorgen Bergstrom</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Jorgen Bergstrom</copyright><lastBuildDate>Fri, 06 Nov 2026 00:00:00 -0500</lastBuildDate><atom:link href="https://bergstrom.org/tags/gradient-boosting/" rel="self" type="application/rss+xml"/><item><title>The 98% Was an Illusion: Five Ways to Classify a Polymer Curve</title><link>https://bergstrom.org/posts/polymer_curve_classifier_comparison/</link><pubDate>Fri, 06 Nov 2026 00:00:00 -0500</pubDate><author>Jorgen Bergstrom</author><guid>https://bergstrom.org/posts/polymer_curve_classifier_comparison/</guid><description>Introduction In an earlier article I trained a small CNN to decide whether an axis-free stress-strain image came from a rubber or a thermoplastic. The headline number was about 98% validation accuracy. I also added a caveat: because many materials had several curves — different strain rates, loading directions, or cyclic tests — curves from the same material could end up in both the training and validation sets. That kind of leakage makes a model look better than it really is.</description></item></channel></rss>