<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Integrity on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/data-integrity/</link><description>Recent content in Data Integrity 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/data-integrity/index.xml" rel="self" type="application/rss+xml"/><item><title>Leakage</title><link>https://terms-en.ai-term-hub.com/en/terms/leakage/</link><pubDate>Sat, 18 Jul 2026 10:04:43 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/leakage/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Data leakage is a critical error in machine learning where the model gains access to information during training that would not be available at prediction time. This often happens through improper data preprocessing, such as scaling before splitting, or including target-related features in the input set. It results in models that appear highly accurate on validation sets but fail catastrophically in real-world deployment because they rely on impossible-to-obtain data.&lt;/p></description></item></channel></rss>