<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on Jackie Valeri</title><link>https://jackievaleri.github.io/tags/machine-learning/</link><description>Recent content in Machine Learning on Jackie Valeri</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Fri, 06 Nov 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://jackievaleri.github.io/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Just throw it away? Class imbalance lessons from molecular machine learning to meatballs</title><link>https://jackievaleri.github.io/posts/scipy-negative-data/</link><pubDate>Fri, 06 Nov 2026 00:00:00 +0000</pubDate><guid>https://jackievaleri.github.io/posts/scipy-negative-data/</guid><description>&lt;p&gt;&lt;a href="https://www.youtube.com/watch?v=i9le8tKaAqY" target="_blank" rel="noopener"&gt;YouTube talk&lt;/a&gt; | &lt;a href="https://github.com/jackievaleri/negative_ml" target="_blank" rel="noopener"&gt;GitHub repository&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;One of the challenges in drug discovery is that most of the things you screen are not going to work. In antibiotic discovery, for example, you might test thousands of molecules in the lab and find that only a single digit percentage have the antibacterial activity you&amp;rsquo;re looking for (and that would be pretty impressive, honestly!). That&amp;rsquo;s a lot of negative data to collect. But do we really need all of it?&lt;/p&gt;</description></item></channel></rss>