<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Validation on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/validation/</link><description>Recent content in Validation 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/validation/index.xml" rel="self" type="application/rss+xml"/><item><title>held-out</title><link>https://terms-en.ai-term-hub.com/en/terms/held_out/</link><pubDate>Sat, 18 Jul 2026 09:38:34 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/held_out/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>A &amp;lsquo;held-out&amp;rsquo; dataset consists of examples intentionally excluded from the training phase of a machine learning model. This subset is used to assess how well the model generalizes to unseen data, providing an unbiased estimate of performance. It is crucial for hyperparameter tuning and validating that the model has not merely memorized the training data, thereby helping to detect overfitting before final deployment.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Data samples reserved from the training set to evaluate model performance and prevent overfitting during development.&lt;/p></description></item><item><title>Evidence</title><link>https://terms-en.ai-term-hub.com/en/terms/evidence/</link><pubDate>Sat, 18 Jul 2026 09:32:12 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/evidence/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>In artificial intelligence, evidence refers to empirical data, statistical results, or observable outcomes that substantiate claims about model behavior, accuracy, or effectiveness. It serves as the foundation for decision-making processes, allowing researchers and engineers to verify whether a machine learning algorithm has learned the intended patterns from its training data. Without robust evidence, AI systems lack credibility and reliability in practical applications.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Data or information used to support a hypothesis or validate an AI model&amp;rsquo;s performance.&lt;/p></description></item></channel></rss>