<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evaluation Metrics on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/evaluation-metrics/</link><description>Recent content in Evaluation Metrics on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/evaluation-metrics/index.xml" rel="self" type="application/rss+xml"/><item><title>Phi coefficient</title><link>https://terms-en.ai-term-hub.com/en/terms/phi_coefficient/</link><pubDate>Sat, 18 Jul 2026 10:10:59 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/phi_coefficient/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>The Phi coefficient (φ) is a measure of association for two binary variables, serving as the Pearson correlation coefficient for dichotomous variables. It ranges from -1 to +1, where 0 indicates no association, +1 indicates perfect positive association, and -1 indicates perfect negative association. It is widely used in contingency table analysis to determine the strength of the relationship between two categorical features, particularly in classification tasks involving binary outcomes.&lt;/p></description></item></channel></rss>